diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
new file mode 100644
index 00000000..8a2880a8
--- /dev/null
+++ b/.github/workflows/build.yaml
@@ -0,0 +1,164 @@
+name: Build and push docker image to registry
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - 'main'
+    tags:
+      - 'v*'
+  pull_request:
+    paths:
+      - ".github/workflows/build.yaml"
+      - ".github/workflows/matrix.json"
+      - "integration-tests/**"
+      - "backends/**"
+      - "core/**"
+      - "router/**"
+      - "Cargo.lock"
+      - "rust-toolchain.toml"
+      - "Dockerfile"
+    branches:
+      - 'main'
+
+jobs:
+  matrix:
+    runs-on: ubuntu-latest
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - id: set-matrix
+        run: |
+          branchName=$(echo '${{ github.ref }}' | sed 's,refs/heads/,,g')
+          matrix=$(jq --arg branchName "$branchName" 'map(. | select((.runOn==$branchName) or (.runOn=="always")) )' .github/workflows/matrix.json)
+          echo "{\"include\":$(echo $matrix)}"
+          echo ::set-output name=matrix::{\"include\":$(echo $matrix)}\"
+
+  build-and-push-image:
+    needs: matrix
+    strategy:
+      matrix: ${{fromJson(needs.matrix.outputs.matrix)}}
+    concurrency:
+      group: ${{ github.workflow }}-${{ github.job }}-${{matrix.name}}-${{ github.head_ref || github.run_id }}
+      cancel-in-progress: true
+    runs-on:
+      group: aws-highmemory-32-plus-priv
+    permissions:
+      contents: write
+      packages: write
+      # This is used to complete the identity challenge
+      # with sigstore/fulcio when running outside of PRs.
+      id-token: write
+      security-events: write
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Initialize Docker Buildx
+        uses: docker/setup-buildx-action@v3
+        with:
+          install: true
+          buildkitd-config: /tmp/buildkitd.toml
+
+      - name: Configure sccache
+        uses: actions/github-script@v6
+        with:
+          script: |
+            core.exportVariable('ACTIONS_CACHE_URL', process.env.ACTIONS_CACHE_URL || '');
+            core.exportVariable('ACTIONS_RUNTIME_TOKEN', process.env.ACTIONS_RUNTIME_TOKEN || '');
+
+      - name: Inject slug/short variables
+        uses: rlespinasse/github-slug-action@v4
+
+      - name: Login to internal Container Registry
+        if: github.event_name != 'pull_request'
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.REGISTRY_USERNAME }}
+          password: ${{ secrets.REGISTRY_PASSWORD }}
+          registry: registry.internal.huggingface.tech
+
+      - name: Login to GitHub Container Registry
+        if: github.event_name != 'pull_request'
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: |
+            registry.internal.huggingface.tech/api-inference/text-embeddings-inference
+            ghcr.io/huggingface/text-embeddings-inference
+          flavor: |
+            latest=false
+          tags: |
+            type=semver,pattern=${{ matrix.imageNamePrefix }}{{version}}
+            type=semver,pattern=${{ matrix.imageNamePrefix }}{{major}}.{{minor}}
+            type=raw,value=${{ matrix.imageNamePrefix }}latest
+            type=raw,value=${{ matrix.imageNamePrefix }}sha-${{ env.GITHUB_SHA_SHORT }}
+
+      - name: Build and push Docker image
+        id: build-and-push
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          file: ${{ matrix.dockerfile }}
+          push: ${{ github.event_name != 'pull_request' }}
+          platforms: 'linux/amd64'
+          build-args: |
+            SCCACHE_GHA_ENABLED=${{ matrix.sccache }}
+            ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
+            ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
+            CUDA_COMPUTE_CAP=${{ matrix.cudaComputeCap }}
+            GIT_SHA=${{ env.GITHUB_SHA }}
+            DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
+            ${{matrix.extraBuildArgs}}
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=s3,region=us-east-1,bucket=ci-docker-buildx-cache,name=text-embeddings-inference-cache-${{matrix.name}},access_key_id=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_ACCESS_KEY_ID }},secret_access_key=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_SECRET_ACCESS_KEY }},mode=max
+          cache-to: type=s3,region=us-east-1,bucket=ci-docker-buildx-cache,name=text-embeddings-inference-cache-${{matrix.name}},access_key_id=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_ACCESS_KEY_ID }},secret_access_key=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_SECRET_ACCESS_KEY }},mode=max
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta-grpc
+        if: ${{ matrix.grpc }}
+        uses: docker/metadata-action@v5
+        with:
+          images: |
+            registry.internal.huggingface.tech/api-inference/text-embeddings-inference
+            ghcr.io/huggingface/text-embeddings-inference
+          flavor: |
+            latest=false
+          tags: |
+            type=semver,pattern=${{ matrix.imageNamePrefix }}{{version}}-grpc
+            type=semver,pattern=${{ matrix.imageNamePrefix }}{{major}}.{{minor}}-grpc
+            type=raw,value=${{ matrix.imageNamePrefix }}latest-grpc
+            type=raw,value=${{ matrix.imageNamePrefix }}sha-${{ env.GITHUB_SHA_SHORT }}-grpc
+
+      - name: Build and push Docker image
+        id: build-and-push-grpc
+        if: ${{ matrix.grpc }}
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          target: grpc
+          file: ${{ matrix.dockerfile }}
+          push: ${{ github.event_name != 'pull_request' }}
+          platforms: 'linux/amd64'
+          build-args: |
+            SCCACHE_GHA_ENABLED=${{ matrix.sccache }}
+            ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
+            ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
+            CUDA_COMPUTE_CAP=${{ matrix.cudaComputeCap }}
+            GIT_SHA=${{ env.GITHUB_SHA }}
+            DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
+            ${{matrix.extraBuildArgs}}
+          tags: ${{ steps.meta-grpc.outputs.tags }}
+          labels: ${{ steps.meta-grpc.outputs.labels }}
+          cache-from: type=s3,region=us-east-1,bucket=ci-docker-buildx-cache,name=text-embeddings-inference-cache-${{matrix.name}},access_key_id=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_ACCESS_KEY_ID }},secret_access_key=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_SECRET_ACCESS_KEY }},mode=max
diff --git a/.github/workflows/build_75.yaml b/.github/workflows/build_75.yaml
deleted file mode 100644
index 6d941d03..00000000
--- a/.github/workflows/build_75.yaml
+++ /dev/null
@@ -1,136 +0,0 @@
- name: Build and push Cuda Turing docker image to registry
-
- on:
-   workflow_dispatch:
-   push:
-     branches:
-       - 'main'
-     tags:
-       - 'v*'
-
- jobs:
-   build-and-push-image:
-     concurrency:
-       group: ${{ github.workflow }}-${{ github.job }}-75-${{ github.head_ref || github.run_id }}
-       cancel-in-progress: true
-     runs-on: [self-hosted, intel-cpu, 32-cpu, 256-ram, ci]
-     permissions:
-       contents: write
-       packages: write
-       # This is used to complete the identity challenge
-       # with sigstore/fulcio when running outside of PRs.
-       id-token: write
-       security-events: write
-     steps:
-       - name: Checkout repository
-         uses: actions/checkout@v3
-
-       - name: Tailscale
-         uses: huggingface/tailscale-action@main
-         with:
-           authkey: ${{ secrets.TAILSCALE_AUTHKEY }}
-
-       - name: Initialize Docker Buildx
-         uses: docker/setup-buildx-action@v2.0.0
-         with:
-           install: true
-           config-inline: |
-             [registry."docker.io"]
-               mirrors = ["registry.github-runners.huggingface.tech"]
-
-       - name: Configure sccache
-         uses: actions/github-script@v6
-         with:
-           script: |
-             core.exportVariable('ACTIONS_CACHE_URL', process.env.ACTIONS_CACHE_URL || '');
-             core.exportVariable('ACTIONS_RUNTIME_TOKEN', process.env.ACTIONS_RUNTIME_TOKEN || '');
-
-       - name: Inject slug/short variables
-         uses: rlespinasse/github-slug-action@v4.4.1
-
-       - name: Login to GitHub Container Registry
-         if: github.event_name != 'pull_request'
-         uses: docker/login-action@v2
-         with:
-           registry: ghcr.io
-           username: ${{ github.actor }}
-           password: ${{ secrets.GITHUB_TOKEN }}
-
-       - name: Login to internal Container Registry
-         uses: docker/login-action@v2.1.0
-         with:
-           username: ${{ secrets.TAILSCALE_DOCKER_USERNAME }}
-           password: ${{ secrets.TAILSCALE_DOCKER_PASSWORD }}
-           registry: registry.internal.huggingface.tech
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-75
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=turing-{{version}}
-             type=semver,pattern=turing-{{major}}.{{minor}}
-             type=raw,value=turing-latest
-             type=raw,value=turing-sha-${{ env.GITHUB_SHA_SHORT }}
-
-       - name: Build and push Docker image
-         id: build-and-push-75
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           file: Dockerfile-cuda
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             CUDA_COMPUTE_CAP=75
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-             DEFAULT_USE_FLASH_ATTENTION=False
-           tags: ${{ steps.meta-75.outputs.tags }}
-           labels: ${{ steps.meta-75.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-75,mode=max
-           cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-75,mode=max
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-75-grpc
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=turing-{{version}}-grpc
-             type=semver,pattern=turing-{{major}}.{{minor}}-grpc
-             type=raw,value=turing-latest-grpc
-             type=raw,value=turing-sha-${{ env.GITHUB_SHA_SHORT }}-grpc
-
-       - name: Build and push Docker image
-         id: build-and-push-75-grpc
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           target: grpc
-           file: Dockerfile-cuda
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             CUDA_COMPUTE_CAP=75
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-             DEFAULT_USE_FLASH_ATTENTION=False
-           tags: ${{ steps.meta-75-grpc.outputs.tags }}
-           labels: ${{ steps.meta-75-grpc.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-75,mode=max
diff --git a/.github/workflows/build_80.yaml b/.github/workflows/build_80.yaml
deleted file mode 100644
index 7a41c117..00000000
--- a/.github/workflows/build_80.yaml
+++ /dev/null
@@ -1,146 +0,0 @@
- name: Build and push Cuda Ampere docker image to registry
-
- on:
-   workflow_dispatch:
-   push:
-     branches:
-       - 'main'
-     tags:
-       - 'v*'
-   pull_request:
-     paths:
-       - ".github/workflows/build.yaml"
-#       - "integration-tests/**"
-       - "backends/**"
-       - "core/**"
-       - "router/**"
-       - "Cargo.lock"
-       - "rust-toolchain.toml"
-       - "Dockerfile"
-     branches:
-       - 'main'
-
- jobs:
-   build-and-push-image:
-     concurrency:
-       group: ${{ github.workflow }}-${{ github.job }}-80-${{ github.head_ref || github.run_id }}
-       cancel-in-progress: true
-     runs-on: [self-hosted, intel-cpu, 32-cpu, 256-ram, ci]
-     permissions:
-       contents: write
-       packages: write
-       # This is used to complete the identity challenge
-       # with sigstore/fulcio when running outside of PRs.
-       id-token: write
-       security-events: write
-     steps:
-       - name: Checkout repository
-         uses: actions/checkout@v3
-
-       - name: Tailscale
-         uses: huggingface/tailscale-action@main
-         with:
-           authkey: ${{ secrets.TAILSCALE_AUTHKEY }}
-
-       - name: Initialize Docker Buildx
-         uses: docker/setup-buildx-action@v2.0.0
-         with:
-           install: true
-           config-inline: |
-             [registry."docker.io"]
-               mirrors = ["registry.github-runners.huggingface.tech"]
-
-       - name: Configure sccache
-         uses: actions/github-script@v6
-         with:
-           script: |
-             core.exportVariable('ACTIONS_CACHE_URL', process.env.ACTIONS_CACHE_URL || '');
-             core.exportVariable('ACTIONS_RUNTIME_TOKEN', process.env.ACTIONS_RUNTIME_TOKEN || '');
-
-       - name: Inject slug/short variables
-         uses: rlespinasse/github-slug-action@v4.4.1
-
-       - name: Login to GitHub Container Registry
-         if: github.event_name != 'pull_request'
-         uses: docker/login-action@v2
-         with:
-           registry: ghcr.io
-           username: ${{ github.actor }}
-           password: ${{ secrets.GITHUB_TOKEN }}
-
-       - name: Login to internal Container Registry
-         uses: docker/login-action@v2.1.0
-         with:
-           username: ${{ secrets.TAILSCALE_DOCKER_USERNAME }}
-           password: ${{ secrets.TAILSCALE_DOCKER_PASSWORD }}
-           registry: registry.internal.huggingface.tech
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-80
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern={{version}}
-             type=semver,pattern={{major}}.{{minor}}
-             type=raw,value=latest
-             type=raw,value=sha-${{ env.GITHUB_SHA_SHORT }}
-
-       - name: Build and push Docker image
-         id: build-and-push-80
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           file: Dockerfile-cuda
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             CUDA_COMPUTE_CAP=80
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta-80.outputs.tags }}
-           labels: ${{ steps.meta-80.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-80,mode=max
-           cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-80,mode=max
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-80-grpc
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern={{version}}-grpc
-             type=semver,pattern={{major}}.{{minor}}-grpc
-             type=raw,value=latest-grpc
-             type=raw,value=sha-${{ env.GITHUB_SHA_SHORT }}-grpc
-
-       - name: Build and push Docker image
-         id: build-and-push-80-grpc
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           target: grpc
-           file: Dockerfile-cuda
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             CUDA_COMPUTE_CAP=80
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta-80-grpc.outputs.tags }}
-           labels: ${{ steps.meta-80-grpc.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-80,mode=max
diff --git a/.github/workflows/build_86.yaml b/.github/workflows/build_86.yaml
deleted file mode 100644
index 16c182ec..00000000
--- a/.github/workflows/build_86.yaml
+++ /dev/null
@@ -1,134 +0,0 @@
- name: Build and push Cuda A10 docker image to registry
-
- on:
-   workflow_dispatch:
-   push:
-     branches:
-       - 'main'
-     tags:
-       - 'v*'
-
- jobs:
-   build-and-push-image:
-     concurrency:
-       group: ${{ github.workflow }}-${{ github.job }}-86-${{ github.head_ref || github.run_id }}
-       cancel-in-progress: true
-     runs-on: [self-hosted, intel-cpu, 32-cpu, 256-ram, ci]
-     permissions:
-       contents: write
-       packages: write
-       # This is used to complete the identity challenge
-       # with sigstore/fulcio when running outside of PRs.
-       id-token: write
-       security-events: write
-     steps:
-       - name: Checkout repository
-         uses: actions/checkout@v3
-
-       - name: Tailscale
-         uses: huggingface/tailscale-action@main
-         with:
-           authkey: ${{ secrets.TAILSCALE_AUTHKEY }}
-
-       - name: Initialize Docker Buildx
-         uses: docker/setup-buildx-action@v2.0.0
-         with:
-           install: true
-           config-inline: |
-             [registry."docker.io"]
-               mirrors = ["registry.github-runners.huggingface.tech"]
-
-       - name: Configure sccache
-         uses: actions/github-script@v6
-         with:
-           script: |
-             core.exportVariable('ACTIONS_CACHE_URL', process.env.ACTIONS_CACHE_URL || '');
-             core.exportVariable('ACTIONS_RUNTIME_TOKEN', process.env.ACTIONS_RUNTIME_TOKEN || '');
-
-       - name: Inject slug/short variables
-         uses: rlespinasse/github-slug-action@v4.4.1
-
-       - name: Login to GitHub Container Registry
-         if: github.event_name != 'pull_request'
-         uses: docker/login-action@v2
-         with:
-           registry: ghcr.io
-           username: ${{ github.actor }}
-           password: ${{ secrets.GITHUB_TOKEN }}
-
-       - name: Login to internal Container Registry
-         uses: docker/login-action@v2.1.0
-         with:
-           username: ${{ secrets.TAILSCALE_DOCKER_USERNAME }}
-           password: ${{ secrets.TAILSCALE_DOCKER_PASSWORD }}
-           registry: registry.internal.huggingface.tech
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-86
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=86-{{version}}
-             type=semver,pattern=86-{{major}}.{{minor}}
-             type=raw,value=86-latest,enable=${{ github.ref == format('refs/heads/{0}', github.event.repository.default_branch) }}
-             type=raw,value=86-sha-${{ env.GITHUB_SHA_SHORT }}
-
-       - name: Build and push Docker image
-         id: build-and-push-86
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           file: Dockerfile-cuda
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             CUDA_COMPUTE_CAP=86
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta-86.outputs.tags }}
-           labels: ${{ steps.meta-86.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-86,mode=max
-           cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-86,mode=max
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-86-grpc
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=86-{{version}}-grpc
-             type=semver,pattern=86-{{major}}.{{minor}}-grpc
-             type=raw,value=86-latest-grpc
-             type=raw,value=86-sha-${{ env.GITHUB_SHA_SHORT }}-grpc
-
-       - name: Build and push Docker image
-         id: build-and-push-86-grpc
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           target: grpc
-           file: Dockerfile-cuda
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             CUDA_COMPUTE_CAP=86
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta-86-grpc.outputs.tags }}
-           labels: ${{ steps.meta-86-grpc.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-86,mode=max
diff --git a/.github/workflows/build_89.yaml b/.github/workflows/build_89.yaml
deleted file mode 100644
index 689838f6..00000000
--- a/.github/workflows/build_89.yaml
+++ /dev/null
@@ -1,134 +0,0 @@
- name: Build and push Cuda RTX 4000 series docker image to registry
-
- on:
-   workflow_dispatch:
-   push:
-     branches:
-       - 'main'
-     tags:
-       - 'v*'
-
- jobs:
-   build-and-push-image:
-     concurrency:
-       group: ${{ github.workflow }}-${{ github.job }}-89-${{ github.head_ref || github.run_id }}
-       cancel-in-progress: true
-     runs-on: [self-hosted, intel-cpu, 32-cpu, 256-ram, ci]
-     permissions:
-       contents: write
-       packages: write
-       # This is used to complete the identity challenge
-       # with sigstore/fulcio when running outside of PRs.
-       id-token: write
-       security-events: write
-     steps:
-       - name: Checkout repository
-         uses: actions/checkout@v3
-
-       - name: Tailscale
-         uses: huggingface/tailscale-action@main
-         with:
-           authkey: ${{ secrets.TAILSCALE_AUTHKEY }}
-
-       - name: Initialize Docker Buildx
-         uses: docker/setup-buildx-action@v2.0.0
-         with:
-           install: true
-           config-inline: |
-             [registry."docker.io"]
-               mirrors = ["registry.github-runners.huggingface.tech"]
-
-       - name: Configure sccache
-         uses: actions/github-script@v6
-         with:
-           script: |
-             core.exportVariable('ACTIONS_CACHE_URL', process.env.ACTIONS_CACHE_URL || '');
-             core.exportVariable('ACTIONS_RUNTIME_TOKEN', process.env.ACTIONS_RUNTIME_TOKEN || '');
-
-       - name: Inject slug/short variables
-         uses: rlespinasse/github-slug-action@v4.4.1
-
-       - name: Login to GitHub Container Registry
-         if: github.event_name != 'pull_request'
-         uses: docker/login-action@v2
-         with:
-           registry: ghcr.io
-           username: ${{ github.actor }}
-           password: ${{ secrets.GITHUB_TOKEN }}
-
-       - name: Login to internal Container Registry
-         uses: docker/login-action@v2.1.0
-         with:
-           username: ${{ secrets.TAILSCALE_DOCKER_USERNAME }}
-           password: ${{ secrets.TAILSCALE_DOCKER_PASSWORD }}
-           registry: registry.internal.huggingface.tech
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-89
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=89-{{version}}
-             type=semver,pattern=89-{{major}}.{{minor}}
-             type=raw,value=89-latest,enable=${{ github.ref == format('refs/heads/{0}', github.event.repository.default_branch) }}
-             type=raw,value=89-sha-${{ env.GITHUB_SHA_SHORT }}
-
-       - name: Build and push Docker image
-         id: build-and-push-89
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           file: Dockerfile-cuda
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             CUDA_COMPUTE_CAP=89
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta-89.outputs.tags }}
-           labels: ${{ steps.meta-89.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-89,mode=max
-           cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-89,mode=max
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-89-grpc
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=89-{{version}}-grpc
-             type=semver,pattern=89-{{major}}.{{minor}}-grpc
-             type=raw,value=89-latest-grpc
-             type=raw,value=89-sha-${{ env.GITHUB_SHA_SHORT }}-grpc
-
-       - name: Build and push Docker image
-         id: build-and-push-89-grpc
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           target: grpc
-           file: Dockerfile-cuda
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             CUDA_COMPUTE_CAP=89
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta-89-grpc.outputs.tags }}
-           labels: ${{ steps.meta-89-grpc.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-89,mode=max
diff --git a/.github/workflows/build_90.yaml b/.github/workflows/build_90.yaml
deleted file mode 100644
index 40f05235..00000000
--- a/.github/workflows/build_90.yaml
+++ /dev/null
@@ -1,134 +0,0 @@
- name: Build and push Cuda Hopper docker image to registry
-
- on:
-   workflow_dispatch:
-   push:
-     branches:
-       - 'main'
-     tags:
-       - 'v*'
-
- jobs:
-   build-and-push-image:
-     concurrency:
-       group: ${{ github.workflow }}-${{ github.job }}-90-${{ github.head_ref || github.run_id }}
-       cancel-in-progress: true
-     runs-on: [self-hosted, intel-cpu, 32-cpu, 256-ram, ci]
-     permissions:
-       contents: write
-       packages: write
-       # This is used to complete the identity challenge
-       # with sigstore/fulcio when running outside of PRs.
-       id-token: write
-       security-events: write
-     steps:
-       - name: Checkout repository
-         uses: actions/checkout@v3
-
-       - name: Tailscale
-         uses: huggingface/tailscale-action@main
-         with:
-           authkey: ${{ secrets.TAILSCALE_AUTHKEY }}
-
-       - name: Initialize Docker Buildx
-         uses: docker/setup-buildx-action@v2.0.0
-         with:
-           install: true
-           config-inline: |
-             [registry."docker.io"]
-               mirrors = ["registry.github-runners.huggingface.tech"]
-
-       - name: Configure sccache
-         uses: actions/github-script@v6
-         with:
-           script: |
-             core.exportVariable('ACTIONS_CACHE_URL', process.env.ACTIONS_CACHE_URL || '');
-             core.exportVariable('ACTIONS_RUNTIME_TOKEN', process.env.ACTIONS_RUNTIME_TOKEN || '');
-
-       - name: Inject slug/short variables
-         uses: rlespinasse/github-slug-action@v4.4.1
-
-       - name: Login to GitHub Container Registry
-         if: github.event_name != 'pull_request'
-         uses: docker/login-action@v2
-         with:
-           registry: ghcr.io
-           username: ${{ github.actor }}
-           password: ${{ secrets.GITHUB_TOKEN }}
-
-       - name: Login to internal Container Registry
-         uses: docker/login-action@v2.1.0
-         with:
-           username: ${{ secrets.TAILSCALE_DOCKER_USERNAME }}
-           password: ${{ secrets.TAILSCALE_DOCKER_PASSWORD }}
-           registry: registry.internal.huggingface.tech
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-90
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=hopper-{{version}}
-             type=semver,pattern=hopper-{{major}}.{{minor}}
-             type=raw,value=hopper-latest,enable=${{ github.ref == format('refs/heads/{0}', github.event.repository.default_branch) }}
-             type=raw,value=hopper-sha-${{ env.GITHUB_SHA_SHORT }}
-
-       - name: Build and push Docker image
-         id: build-and-push-90
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           file: Dockerfile-cuda
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             CUDA_COMPUTE_CAP=90
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta-90.outputs.tags }}
-           labels: ${{ steps.meta-90.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-90,mode=max
-           cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-90,mode=max
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-90-grpc
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=hopper-{{version}}-grpc
-             type=semver,pattern=hopper-{{major}}.{{minor}}-grpc
-             type=raw,value=hopper-latest-grpc
-             type=raw,value=hopper-sha-${{ env.GITHUB_SHA_SHORT }}-grpc
-
-       - name: Build and push Docker image
-         id: build-and-push-90-grpc
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           target: grpc
-           file: Dockerfile-cuda
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             CUDA_COMPUTE_CAP=90
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta-90-grpc.outputs.tags }}
-           labels: ${{ steps.meta-90-grpc.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-90,mode=max
diff --git a/.github/workflows/build_all.yaml b/.github/workflows/build_all.yaml
deleted file mode 100644
index 58aa7b09..00000000
--- a/.github/workflows/build_all.yaml
+++ /dev/null
@@ -1,119 +0,0 @@
- name: Build and push Cuda docker image to registry
-
- on:
-   workflow_dispatch:
-   push:
-     branches:
-       - 'main'
-     tags:
-       - 'v*'
-
- jobs:
-   build-and-push-image:
-     concurrency:
-       group: ${{ github.workflow }}-${{ github.job }}-all-${{ github.head_ref || github.run_id }}
-       cancel-in-progress: true
-     runs-on: [self-hosted, intel-cpu, 32-cpu, 256-ram, ci]
-     permissions:
-       contents: write
-       packages: write
-       # This is used to complete the identity challenge
-       # with sigstore/fulcio when running outside of PRs.
-       id-token: write
-       security-events: write
-     steps:
-       - name: Checkout repository
-         uses: actions/checkout@v3
-
-       - name: Tailscale
-         uses: huggingface/tailscale-action@v1
-         with:
-           authkey: ${{ secrets.TAILSCALE_AUTHKEY }}
-
-       - name: Initialize Docker Buildx
-         uses: docker/setup-buildx-action@v2.0.0
-         with:
-           install: true
-           config-inline: |
-             [registry."docker.io"]
-               mirrors = ["registry.github-runners.huggingface.tech"]
-
-       - name: Inject slug/short variables
-         uses: rlespinasse/github-slug-action@v4.4.1
-
-       - name: Login to GitHub Container Registry
-         if: github.event_name != 'pull_request'
-         uses: docker/login-action@v2
-         with:
-           registry: ghcr.io
-           username: ${{ github.actor }}
-           password: ${{ secrets.GITHUB_TOKEN }}
-
-       - name: Login to internal Container Registry
-         uses: docker/login-action@v2.1.0
-         with:
-           username: ${{ secrets.TAILSCALE_DOCKER_USERNAME }}
-           password: ${{ secrets.TAILSCALE_DOCKER_PASSWORD }}
-           registry: registry.internal.huggingface.tech
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=cuda-{{version}}
-             type=semver,pattern=cuda-{{major}}.{{minor}}
-             type=raw,value=cuda-latest,enable=${{ github.ref == format('refs/heads/{0}', github.event.repository.default_branch) }}
-             type=raw,value=cuda-sha-${{ env.GITHUB_SHA_SHORT }}
-
-       - name: Build and push Docker image
-         id: build-and-push
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           file: Dockerfile-cuda-all
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta.outputs.tags }}
-           labels: ${{ steps.meta.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-all,mode=max
-           cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-all,mode=max
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-sagemaker
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference/sagemaker
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=cuda-{{version}}
-             type=semver,pattern=cuda-{{major}}.{{minor}}
-             type=raw,value=cuda-latest,enable=${{ github.ref == format('refs/heads/{0}', github.event.repository.default_branch) }}
-             type=raw,value=cuda-sha-${{ env.GITHUB_SHA_SHORT }}
-
-       - name: Build and push Docker image
-         id: build-and-push-sagemaker
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           file: Dockerfile-cuda-all
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           target: sagemaker
-           build-args: |
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta-sagemaker.outputs.tags }}
-           labels: ${{ steps.meta-sagemaker.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-all,mode=max
-           cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-all,mode=max
diff --git a/.github/workflows/build_cpu.yaml b/.github/workflows/build_cpu.yaml
deleted file mode 100644
index 3cf87f5a..00000000
--- a/.github/workflows/build_cpu.yaml
+++ /dev/null
@@ -1,144 +0,0 @@
- name: Build and push CPU docker image to registry
-
- on:
-   workflow_dispatch:
-   push:
-     branches:
-       - 'main'
-     tags:
-       - 'v*'
-   pull_request:
-     paths:
-       - ".github/workflows/build.yaml"
-#       - "integration-tests/**"
-       - "backends/**"
-       - "core/**"
-       - "router/**"
-       - "Cargo.lock"
-       - "rust-toolchain.toml"
-       - "Dockerfile"
-     branches:
-       - 'main'
-
- jobs:
-   build-and-push-image:
-     concurrency:
-       group: ${{ github.workflow }}-${{ github.job }}-cpu-${{ github.head_ref || github.run_id }}
-       cancel-in-progress: true
-     runs-on: [self-hosted, intel-cpu, 32-cpu, 256-ram, ci]
-     permissions:
-       contents: write
-       packages: write
-       # This is used to complete the identity challenge
-       # with sigstore/fulcio when running outside of PRs.
-       id-token: write
-       security-events: write
-     steps:
-       - name: Checkout repository
-         uses: actions/checkout@v3
-
-       - name: Configure sccache
-         uses: actions/github-script@v6
-         with:
-           script: |
-             core.exportVariable('ACTIONS_CACHE_URL', process.env.ACTIONS_CACHE_URL || '');
-             core.exportVariable('ACTIONS_RUNTIME_TOKEN', process.env.ACTIONS_RUNTIME_TOKEN || '');
-
-       - name: Inject slug/short variables
-         uses: rlespinasse/github-slug-action@v4.4.1
-
-       - name: Tailscale
-         uses: huggingface/tailscale-action@main
-         with:
-           authkey: ${{ secrets.TAILSCALE_AUTHKEY }}
-
-       - name: Initialize Docker Buildx
-         uses: docker/setup-buildx-action@v2.0.0
-         with:
-           install: true
-           config-inline: |
-             [registry."docker.io"]
-               mirrors = ["registry.github-runners.huggingface.tech"]
-
-       - name: Login to GitHub Container Registry
-         if: github.event_name != 'pull_request'
-         uses: docker/login-action@v2
-         with:
-           registry: ghcr.io
-           username: ${{ github.actor }}
-           password: ${{ secrets.GITHUB_TOKEN }}
-
-       - name: Login to internal Container Registry
-         uses: docker/login-action@v2.1.0
-         with:
-           username: ${{ secrets.TAILSCALE_DOCKER_USERNAME }}
-           password: ${{ secrets.TAILSCALE_DOCKER_PASSWORD }}
-           registry: registry.internal.huggingface.tech
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-cpu
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=cpu-{{version}}
-             type=semver,pattern=cpu-{{major}}.{{minor}}
-             type=raw,value=cpu-latest,enable=${{ github.ref == format('refs/heads/{0}', github.event.repository.default_branch) }}
-             type=raw,value=cpu-sha-${{ env.GITHUB_SHA_SHORT }}
-
-       - name: Build and push Docker image
-         id: build-and-push-cpu
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           file: Dockerfile
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta-cpu.outputs.tags }}
-           labels: ${{ steps.meta-cpu.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-cpu,mode=max
-           cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-cpu,mode=max
-
-       - name: Extract metadata (tags, labels) for Docker
-         id: meta-cpu-grpc
-         uses: docker/metadata-action@v4.3.0
-         with:
-           images: |
-             registry.internal.huggingface.tech/api-inference/text-embeddings-inference
-             ghcr.io/huggingface/text-embeddings-inference
-           flavor: |
-             latest=false
-           tags: |
-             type=semver,pattern=cpu-{{version}}-grpc
-             type=semver,pattern=cpu-{{major}}.{{minor}}-grpc
-             type=raw,value=cpu-latest-grpc
-             type=raw,value=cpu-sha-${{ env.GITHUB_SHA_SHORT }}-grpc
-
-       - name: Build and push Docker image
-         id: build-and-push-cpu-grpc
-         uses: docker/build-push-action@v4
-         with:
-           context: .
-           target: grpc
-           file: Dockerfile
-           push: ${{ github.event_name != 'pull_request' }}
-           platforms: 'linux/amd64'
-           build-args: |
-             SCCACHE_GHA_ENABLED=on
-             ACTIONS_CACHE_URL=${{ env.ACTIONS_CACHE_URL }}
-             ACTIONS_RUNTIME_TOKEN=${{ env.ACTIONS_RUNTIME_TOKEN }}
-             GIT_SHA=${{ env.GITHUB_SHA }}
-             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}
-           tags: ${{ steps.meta-cpu-grpc.outputs.tags }}
-           labels: ${{ steps.meta-cpu-grpc.outputs.labels }}
-           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/text-embeddings-inference:cache-cpu,mode=max
diff --git a/.github/workflows/matrix.json b/.github/workflows/matrix.json
new file mode 100644
index 00000000..230bd986
--- /dev/null
+++ b/.github/workflows/matrix.json
@@ -0,0 +1,64 @@
+[
+  {
+    "name": "turing",
+    "imageNamePrefix": "turing-",
+    "runOn": "main",
+    "sccache": true,
+    "cudaComputeCap": 75,
+    "extraBuildArgs": "DEFAULT_USE_FLASH_ATTENTION=False",
+    "grpc": true,
+    "dockerfile": "Dockerfile-cuda"
+  },
+  {
+    "name": "ampere",
+    "imageNamePrefix": "",
+    "runOn": "always",
+    "sccache": true,
+    "cudaComputeCap": 80,
+    "grpc": true,
+    "dockerfile": "Dockerfile-cuda"
+  },
+  {
+    "name": "a10",
+    "imageNamePrefix": "86-",
+    "runOn": "main",
+    "sccache": true,
+    "cudaComputeCap": 86,
+    "grpc": true,
+    "dockerfile": "Dockerfile-cuda"
+  },
+  {
+    "name": "RTX 4000",
+    "imageNamePrefix": "89-",
+    "runOn": "main",
+    "sccache": true,
+    "cudaComputeCap": 89,
+    "grpc": true,
+    "dockerfile": "Dockerfile-cuda"
+  },
+  {
+    "name": "Hopper",
+    "imageNamePrefix": "hopper-",
+    "runOn": "main",
+    "sccache": true,
+    "cudaComputeCap": 90,
+    "grpc": true,
+    "dockerfile": "Dockerfile-cuda"
+  },
+  {
+    "name": "All",
+    "imageNamePrefix": "cuda-",
+    "runOn": "main",
+    "sccache": false,
+    "grpc": false,
+    "dockerfile": "Dockerfile-cuda-all"
+  },
+  {
+    "name": "cpu",
+    "imageNamePrefix": "cpu-",
+    "runOn": "main",
+    "sccache": true,
+    "grpc": true,
+    "dockerfile": "Dockerfile"
+  }
+]
diff --git a/Cargo.lock b/Cargo.lock
index 2b494010..38c868f3 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -10,9 +10,9 @@ checksum = "415ed64958754dbe991900f3940677e6a7eefb4d7367afd70d642677b0c7d19d"
 
 [[package]]
 name = "addr2line"
-version = "0.21.0"
+version = "0.22.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8a30b2e23b9e17a9f90641c7ab1549cd9b44f296d3ccbf309d2863cfe398a0cb"
+checksum = "6e4503c46a5c0c7844e948c9a4d6acd9f50cccb4de1c48eb9e291ea17470c678"
 dependencies = [
  "gimli",
 ]
@@ -61,47 +61,48 @@ dependencies = [
 
 [[package]]
 name = "anstream"
-version = "0.6.13"
+version = "0.6.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d96bd03f33fe50a863e394ee9718a706f988b9079b20c3784fb726e7678b62fb"
+checksum = "418c75fa768af9c03be99d17643f93f79bbba589895012a80e3452a19ddda15b"
 dependencies = [
  "anstyle",
  "anstyle-parse",
  "anstyle-query",
  "anstyle-wincon",
  "colorchoice",
+ "is_terminal_polyfill",
  "utf8parse",
 ]
 
 [[package]]
 name = "anstyle"
-version = "1.0.6"
+version = "1.0.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8901269c6307e8d93993578286ac0edf7f195079ffff5ebdeea6a59ffb7e36bc"
+checksum = "038dfcf04a5feb68e9c60b21c9625a54c2c0616e79b72b0fd87075a056ae1d1b"
 
 [[package]]
 name = "anstyle-parse"
-version = "0.2.3"
+version = "0.2.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c75ac65da39e5fe5ab759307499ddad880d724eed2f6ce5b5e8a26f4f387928c"
+checksum = "c03a11a9034d92058ceb6ee011ce58af4a9bf61491aa7e1e59ecd24bd40d22d4"
 dependencies = [
  "utf8parse",
 ]
 
 [[package]]
 name = "anstyle-query"
-version = "1.0.2"
+version = "1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e28923312444cdd728e4738b3f9c9cac739500909bb3d3c94b43551b16517648"
+checksum = "ad186efb764318d35165f1758e7dcef3b10628e26d41a44bc5550652e6804391"
 dependencies = [
  "windows-sys 0.52.0",
 ]
 
 [[package]]
 name = "anstyle-wincon"
-version = "3.0.2"
+version = "3.0.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1cd54b81ec8d6180e24654d0b371ad22fc3dd083b6ff8ba325b72e00c87660a7"
+checksum = "61a38449feb7068f52bb06c12759005cf459ee52bb4adc1d5a7c4322d716fb19"
 dependencies = [
  "anstyle",
  "windows-sys 0.52.0",
@@ -109,13 +110,34 @@ dependencies = [
 
 [[package]]
 name = "anyhow"
-version = "1.0.82"
+version = "1.0.86"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f538837af36e6f6a9be0faa67f9a314f8119e4e4b5867c6ab40ed60360142519"
+checksum = "b3d1d046238990b9cf5bcde22a3fb3584ee5cf65fb2765f454ed428c7a0063da"
 dependencies = [
  "backtrace",
 ]
 
+[[package]]
+name = "arbitrary"
+version = "1.3.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7d5a26814d8dcb93b0e5a0ff3c6d80a8843bafb21b39e8e18a6f05471870e110"
+dependencies = [
+ "derive_arbitrary",
+]
+
+[[package]]
+name = "async-channel"
+version = "2.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "89b47800b0be77592da0afd425cc03468052844aff33b84e33cc696f64e77b6a"
+dependencies = [
+ "concurrent-queue",
+ "event-listener-strategy",
+ "futures-core",
+ "pin-project-lite",
+]
+
 [[package]]
 name = "async-stream"
 version = "0.3.5"
@@ -135,7 +157,7 @@ checksum = "16e62a023e7c117e27523144c5d2459f4397fcc3cab0085af8e2224f643a0193"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -146,14 +168,47 @@ checksum = "c6fa2087f2753a7da8cc1c0dbfcf89579dd57458e36769de5ac750b4671737ca"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
+[[package]]
+name = "atomic-waker"
+version = "1.1.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1505bd5d3d116872e7271a6d4e16d81d0c8570876c8de68093a09ac269d8aac0"
+
 [[package]]
 name = "autocfg"
-version = "1.2.0"
+version = "1.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f1fdabc7756949593fe60f30ec81974b613357de856987752631dea1e3394c80"
+checksum = "0c4b4d0bd25bd0b74681c0ad21497610ce1b7c91b1022cd21c80c6fbdd9476b0"
+
+[[package]]
+name = "aws-lc-rs"
+version = "1.8.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a8a47f2fb521b70c11ce7369a6c5fa4bd6af7e5d62ec06303875bafe7c6ba245"
+dependencies = [
+ "aws-lc-sys",
+ "mirai-annotations",
+ "paste",
+ "zeroize",
+]
+
+[[package]]
+name = "aws-lc-sys"
+version = "0.19.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2927c7af777b460b7ccd95f8b67acd7b4c04ec8896bf0c8e80ba30523cffc057"
+dependencies = [
+ "bindgen",
+ "cc",
+ "cmake",
+ "dunce",
+ "fs_extra",
+ "libc",
+ "paste",
+]
 
 [[package]]
 name = "axum"
@@ -168,7 +223,7 @@ dependencies = [
  "futures-util",
  "http 0.2.12",
  "http-body 0.4.6",
- "hyper 0.14.28",
+ "hyper 0.14.29",
  "itoa",
  "matchit",
  "memchr",
@@ -196,7 +251,7 @@ dependencies = [
  "http 1.1.0",
  "http-body 1.0.0",
  "http-body-util",
- "hyper 1.3.1",
+ "hyper 1.4.0",
  "hyper-util",
  "itoa",
  "matchit",
@@ -257,25 +312,25 @@ dependencies = [
 
 [[package]]
 name = "axum-tracing-opentelemetry"
-version = "0.17.1"
+version = "0.18.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "91387a3e9f6aa45f112cd05d1e5430f9ba40b51440849e4760a5dd51b736149f"
+checksum = "f26965dca35be1ca4a128177b9302c4c04f0661443621171adb09d002dcdf1d5"
 dependencies = [
  "axum 0.7.5",
  "futures-core",
  "futures-util",
  "http 1.1.0",
- "opentelemetry 0.21.0",
+ "opentelemetry 0.22.0",
  "pin-project-lite",
  "tower",
  "tracing",
- "tracing-opentelemetry 0.22.0",
+ "tracing-opentelemetry 0.23.0",
  "tracing-opentelemetry-instrumentation-sdk",
 ]
 
 [[package]]
 name = "backend-grpc-client"
-version = "1.2.3"
+version = "1.5.1"
 dependencies = [
  "grpc-metadata",
  "prost 0.11.9",
@@ -290,9 +345,9 @@ dependencies = [
 
 [[package]]
 name = "backtrace"
-version = "0.3.71"
+version = "0.3.73"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "26b05800d2e817c8b3b4b54abd461726265fa9789ae34330622f2db9ee696f9d"
+checksum = "5cc23269a4f8976d0a4d2e7109211a419fe30e8d88d677cd60b6bc79c5732e0a"
 dependencies = [
  "addr2line",
  "cc",
@@ -323,9 +378,32 @@ checksum = "9d297deb1925b89f2ccc13d7635fa0714f12c87adce1c75356b39ca9b7178567"
 
 [[package]]
 name = "base64"
-version = "0.22.0"
+version = "0.22.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9475866fec1451be56a3c2400fd081ff546538961565ccb5b7142cbd22bc7a51"
+checksum = "72b3254f16251a8381aa12e40e3c4d2f0199f8c6508fbecb9d91f575e0fbb8c6"
+
+[[package]]
+name = "bindgen"
+version = "0.69.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a00dc851838a2120612785d195287475a3ac45514741da670b735818822129a0"
+dependencies = [
+ "bitflags 2.6.0",
+ "cexpr",
+ "clang-sys",
+ "itertools 0.12.1",
+ "lazy_static",
+ "lazycell",
+ "log",
+ "prettyplease 0.2.20",
+ "proc-macro2",
+ "quote",
+ "regex",
+ "rustc-hash",
+ "shlex",
+ "syn 2.0.68",
+ "which",
+]
 
 [[package]]
 name = "bindgen_cuda"
@@ -361,9 +439,9 @@ checksum = "bef38d45163c2f1dde094a7dfd33ccf595c92905c8f8f4fdc18d06fb1037718a"
 
 [[package]]
 name = "bitflags"
-version = "2.5.0"
+version = "2.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "cf4b9d6a944f767f8e5e0db018570623c85f3d925ac718db4e06d0187adb21c1"
+checksum = "b048fb63fd8b5923fc5aa7b340d8e156aec7ec02f0c78fa8a6ddc2613f6f71de"
 
 [[package]]
 name = "block"
@@ -388,22 +466,22 @@ checksum = "79296716171880943b8470b5f8d03aa55eb2e645a4874bdbb28adb49162e012c"
 
 [[package]]
 name = "bytemuck"
-version = "1.15.0"
+version = "1.16.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5d6d68c57235a3a081186990eca2867354726650f42f7516ca50c28d6281fd15"
+checksum = "b236fc92302c97ed75b38da1f4917b5cdda4984745740f153a5d3059e48d725e"
 dependencies = [
  "bytemuck_derive",
 ]
 
 [[package]]
 name = "bytemuck_derive"
-version = "1.6.0"
+version = "1.7.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4da9a32f3fed317401fa3c862968128267c3106685286e15d5aaa3d7389c2f60"
+checksum = "1ee891b04274a59bd38b412188e24b849617b2e45a0fd8d057deb63e7403761b"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -421,7 +499,7 @@ checksum = "514de17de45fdb8dc022b1a7975556c53c86f9f0aa5f534b98977b171857c2c9"
 [[package]]
 name = "candle-core"
 version = "0.5.0"
-source = "git+https://github.com/OlivierDehaene/candle?rev=33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3#33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3"
+source = "git+https://github.com/OlivierDehaene/candle?rev=7e02ad856104799b73a946ac1e153f0de77feaaf#7e02ad856104799b73a946ac1e153f0de77feaaf"
 dependencies = [
  "accelerate-src",
  "byteorder",
@@ -442,7 +520,7 @@ dependencies = [
  "safetensors",
  "thiserror",
  "yoke",
- "zip",
+ "zip 0.6.6",
 ]
 
 [[package]]
@@ -458,7 +536,7 @@ dependencies = [
 [[package]]
 name = "candle-flash-attn"
 version = "0.5.0"
-source = "git+https://github.com/OlivierDehaene/candle?rev=33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3#33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3"
+source = "git+https://github.com/OlivierDehaene/candle?rev=7e02ad856104799b73a946ac1e153f0de77feaaf#7e02ad856104799b73a946ac1e153f0de77feaaf"
 dependencies = [
  "anyhow",
  "bindgen_cuda",
@@ -481,7 +559,7 @@ dependencies = [
 [[package]]
 name = "candle-kernels"
 version = "0.5.0"
-source = "git+https://github.com/OlivierDehaene/candle?rev=33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3#33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3"
+source = "git+https://github.com/OlivierDehaene/candle?rev=7e02ad856104799b73a946ac1e153f0de77feaaf#7e02ad856104799b73a946ac1e153f0de77feaaf"
 dependencies = [
  "bindgen_cuda",
 ]
@@ -501,7 +579,7 @@ dependencies = [
 [[package]]
 name = "candle-metal-kernels"
 version = "0.5.0"
-source = "git+https://github.com/OlivierDehaene/candle?rev=33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3#33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3"
+source = "git+https://github.com/OlivierDehaene/candle?rev=7e02ad856104799b73a946ac1e153f0de77feaaf#7e02ad856104799b73a946ac1e153f0de77feaaf"
 dependencies = [
  "metal",
  "once_cell",
@@ -512,7 +590,7 @@ dependencies = [
 [[package]]
 name = "candle-nn"
 version = "0.5.0"
-source = "git+https://github.com/OlivierDehaene/candle?rev=33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3#33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3"
+source = "git+https://github.com/OlivierDehaene/candle?rev=7e02ad856104799b73a946ac1e153f0de77feaaf#7e02ad856104799b73a946ac1e153f0de77feaaf"
 dependencies = [
  "accelerate-src",
  "candle-core",
@@ -541,7 +619,7 @@ dependencies = [
 [[package]]
 name = "candle-transformers"
 version = "0.5.0"
-source = "git+https://github.com/OlivierDehaene/candle?rev=33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3#33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3"
+source = "git+https://github.com/OlivierDehaene/candle?rev=7e02ad856104799b73a946ac1e153f0de77feaaf#7e02ad856104799b73a946ac1e153f0de77feaaf"
 dependencies = [
  "byteorder",
  "candle-core",
@@ -558,9 +636,23 @@ dependencies = [
 
 [[package]]
 name = "cc"
-version = "1.0.95"
+version = "1.0.104"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "74b6a57f98764a267ff415d50a25e6e166f3831a5071af4995296ea97d210490"
+dependencies = [
+ "jobserver",
+ "libc",
+ "once_cell",
+]
+
+[[package]]
+name = "cexpr"
+version = "0.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d32a725bc159af97c3e629873bb9f88fb8cf8a4867175f76dc987815ea07c83b"
+checksum = "6fac387a98bb7c37292057cffc56d62ecb629900026402633ae9160df93a8766"
+dependencies = [
+ "nom",
+]
 
 [[package]]
 name = "cfg-if"
@@ -582,11 +674,22 @@ dependencies = [
  "windows-targets 0.52.5",
 ]
 
+[[package]]
+name = "clang-sys"
+version = "1.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0b023947811758c97c59bf9d1c188fd619ad4718dcaa767947df1cadb14f39f4"
+dependencies = [
+ "glob",
+ "libc",
+ "libloading",
+]
+
 [[package]]
 name = "clap"
-version = "4.5.4"
+version = "4.5.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "90bc066a67923782aa8515dbaea16946c5bcc5addbd668bb80af688e53e548a0"
+checksum = "84b3edb18336f4df585bc9aa31dd99c036dfa5dc5e9a2939a722a188f3a8970d"
 dependencies = [
  "clap_builder",
  "clap_derive",
@@ -594,39 +697,57 @@ dependencies = [
 
 [[package]]
 name = "clap_builder"
-version = "4.5.2"
+version = "4.5.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ae129e2e766ae0ec03484e609954119f123cc1fe650337e155d03b022f24f7b4"
+checksum = "c1c09dd5ada6c6c78075d6fd0da3f90d8080651e2d6cc8eb2f1aaa4034ced708"
 dependencies = [
  "anstream",
  "anstyle",
  "clap_lex",
- "strsim 0.11.1",
+ "strsim",
 ]
 
 [[package]]
 name = "clap_derive"
-version = "4.5.4"
+version = "4.5.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "528131438037fd55894f62d6e9f068b8f45ac57ffa77517819645d10aed04f64"
+checksum = "2bac35c6dafb060fd4d275d9a4ffae97917c13a6327903a8be2153cd964f7085"
 dependencies = [
  "heck 0.5.0",
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
 name = "clap_lex"
-version = "0.7.0"
+version = "0.7.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "98cc8fbded0c607b7ba9dd60cd98df59af97e84d24e49c8557331cfc26d301ce"
+checksum = "4b82cf0babdbd58558212896d1a4272303a57bdb245c2bf1147185fb45640e70"
+
+[[package]]
+name = "cmake"
+version = "0.1.50"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a31c789563b815f77f4250caee12365734369f942439b7defd71e18a48197130"
+dependencies = [
+ "cc",
+]
 
 [[package]]
 name = "colorchoice"
-version = "1.0.0"
+version = "1.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0b6a852b24ab71dffc585bcb46eaf7959d175cb865a7152e35b348d1b2960422"
+
+[[package]]
+name = "concurrent-queue"
+version = "2.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "acbf1af155f9b9ef647e42cdc158db4b64a1b61f743629225fde6f3e0be2a7c7"
+checksum = "4ca0197aee26d1ae37445ee532fefce43251d24cc7c166799f4d46817f1d3973"
+dependencies = [
+ "crossbeam-utils",
+]
 
 [[package]]
 name = "console"
@@ -679,18 +800,18 @@ dependencies = [
 
 [[package]]
 name = "crc32fast"
-version = "1.4.0"
+version = "1.4.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b3855a8a784b474f333699ef2bbca9db2c4a1f6d9088a90a2d25b1eb53111eaa"
+checksum = "a97769d94ddab943e4510d138150169a2758b5ef3eb191a9ee688de3e23ef7b3"
 dependencies = [
  "cfg-if",
 ]
 
 [[package]]
 name = "crossbeam-channel"
-version = "0.5.12"
+version = "0.5.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ab3db02a9c5b5121e1e42fbdb1aeb65f5e02624cc58c43f2884c6ccac0b82f95"
+checksum = "33480d6946193aa8033910124896ca395333cae7e2d1113d1fef6c3272217df2"
 dependencies = [
  "crossbeam-utils",
 ]
@@ -716,9 +837,9 @@ dependencies = [
 
 [[package]]
 name = "crossbeam-utils"
-version = "0.8.19"
+version = "0.8.20"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "248e3bacc7dc6baa3b21e405ee045c3047101a49145e7e9eca583ab4c2ca5345"
+checksum = "22ec99545bb0ed0ea7bb9b8e1e9122ea386ff8a48c0922e43f36d45ab09e0e80"
 
 [[package]]
 name = "crunchy"
@@ -746,9 +867,9 @@ dependencies = [
 
 [[package]]
 name = "darling"
-version = "0.20.8"
+version = "0.20.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "54e36fcd13ed84ffdfda6f5be89b31287cbb80c439841fe69e04841435464391"
+checksum = "83b2eb4d90d12bdda5ed17de686c2acb4c57914f8f921b8da7e112b5a36f3fe1"
 dependencies = [
  "darling_core",
  "darling_macro",
@@ -756,27 +877,27 @@ dependencies = [
 
 [[package]]
 name = "darling_core"
-version = "0.20.8"
+version = "0.20.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9c2cf1c23a687a1feeb728783b993c4e1ad83d99f351801977dd809b48d0a70f"
+checksum = "622687fe0bac72a04e5599029151f5796111b90f1baaa9b544d807a5e31cd120"
 dependencies = [
  "fnv",
  "ident_case",
  "proc-macro2",
  "quote",
- "strsim 0.10.0",
- "syn 2.0.60",
+ "strsim",
+ "syn 2.0.68",
 ]
 
 [[package]]
 name = "darling_macro"
-version = "0.20.8"
+version = "0.20.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a668eda54683121533a393014d8692171709ff57a7d61f187b6e782719f8933f"
+checksum = "733cabb43482b1a1b53eee8583c2b9e8684d592215ea83efd305dd31bc2f0178"
 dependencies = [
  "darling_core",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -786,7 +907,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "978747c1d849a7d2ee5e8adc0159961c48fb7e5db2f06af6723b80123bb53856"
 dependencies = [
  "cfg-if",
- "hashbrown 0.14.3",
+ "hashbrown 0.14.5",
  "lock_api",
  "once_cell",
  "parking_lot_core",
@@ -801,6 +922,17 @@ dependencies = [
  "powerfmt",
 ]
 
+[[package]]
+name = "derive_arbitrary"
+version = "1.3.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "67e77553c4162a157adbf834ebae5b415acbecbeafc7a74b0e886657506a7611"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.68",
+]
+
 [[package]]
 name = "derive_builder"
 version = "0.20.0"
@@ -819,7 +951,7 @@ dependencies = [
  "darling",
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -829,7 +961,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "206868b8242f27cecce124c19fd88157fbd0dd334df2587f36417bafbc85097b"
 dependencies = [
  "derive_builder_core",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -872,6 +1004,23 @@ dependencies = [
  "windows-sys 0.48.0",
 ]
 
+[[package]]
+name = "displaydoc"
+version = "0.2.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "97369cbbc041bc366949bc74d34658d6cda5621039731c6310521892a3a20ae0"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.68",
+]
+
+[[package]]
+name = "dunce"
+version = "1.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "56ce8c6da7551ec6c462cbaf3bfbc75131ebbfa1c944aeaa9dab51ca1c5f0c3b"
+
 [[package]]
 name = "dyn-stack"
 version = "0.10.0"
@@ -884,9 +1033,9 @@ dependencies = [
 
 [[package]]
 name = "either"
-version = "1.11.0"
+version = "1.13.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a47c1c47d2f5964e29c61246e81db715514cd532db6b5116a25ea3c03d6780a2"
+checksum = "60b1af1c220855b6ceac025d3f6ecdd2b7c4894bfe9cd9bda4fbb4bc7c0d4cf0"
 
 [[package]]
 name = "encode_unicode"
@@ -912,7 +1061,7 @@ dependencies = [
  "heck 0.4.1",
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -923,9 +1072,9 @@ checksum = "5443807d6dff69373d433ab9ef5378ad8df50ca6298caf15de6e52e24aaf54d5"
 
 [[package]]
 name = "errno"
-version = "0.3.8"
+version = "0.3.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a258e46cdc063eb8519c00b9fc845fc47bcfca4130e2f08e88665ceda8474245"
+checksum = "534c5cf6194dfab3db3242765c03bbe257cf92f22b38f6bc0c58d59108a820ba"
 dependencies = [
  "libc",
  "windows-sys 0.52.0",
@@ -940,6 +1089,27 @@ dependencies = [
  "cc",
 ]
 
+[[package]]
+name = "event-listener"
+version = "5.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6032be9bd27023a771701cc49f9f053c751055f71efb2e0ae5c15809093675ba"
+dependencies = [
+ "concurrent-queue",
+ "parking",
+ "pin-project-lite",
+]
+
+[[package]]
+name = "event-listener-strategy"
+version = "0.5.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0f214dc438f977e6d4e3500aaa277f5ad94ca83fbbd9b1a15713ce2344ccc5a1"
+dependencies = [
+ "event-listener",
+ "pin-project-lite",
+]
+
 [[package]]
 name = "fancy-regex"
 version = "0.13.0"
@@ -947,15 +1117,15 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "531e46835a22af56d1e3b66f04844bed63158bc094a628bec1d321d9b4c44bf2"
 dependencies = [
  "bit-set",
- "regex-automata 0.4.6",
- "regex-syntax 0.8.3",
+ "regex-automata 0.4.7",
+ "regex-syntax 0.8.4",
 ]
 
 [[package]]
 name = "fastrand"
-version = "2.0.2"
+version = "2.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "658bd65b1cf4c852a3cc96f18a8ce7b5640f6b703f905c7d74532294c2a63984"
+checksum = "9fc0510504f03c51ada170672ac806f1f105a88aa97a5281117e1ddc3368e51a"
 
 [[package]]
 name = "filetime"
@@ -965,7 +1135,7 @@ checksum = "1ee447700ac8aa0b2f2bd7bc4462ad686ba06baa6727ac149a2d6277f0d240fd"
 dependencies = [
  "cfg-if",
  "libc",
- "redox_syscall",
+ "redox_syscall 0.4.1",
  "windows-sys 0.52.0",
 ]
 
@@ -977,9 +1147,9 @@ checksum = "0ce7134b9999ecaf8bcd65542e436736ef32ddca1b3e06094cb6ec5755203b80"
 
 [[package]]
 name = "flate2"
-version = "1.0.28"
+version = "1.0.30"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "46303f565772937ffe1d394a4fac6f411c6013172fadde9dcdb1e147a086940e"
+checksum = "5f54427cfd1c7829e2a139fcefea601bf088ebca651d2bf53ebc600eac295dae"
 dependencies = [
  "crc32fast",
  "miniz_oxide",
@@ -1018,7 +1188,7 @@ checksum = "1a5c6c585bc94aaf2c7b51dd4c2ba22680844aba4c687be581871a6f518c5742"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -1042,6 +1212,12 @@ dependencies = [
  "percent-encoding",
 ]
 
+[[package]]
+name = "fs_extra"
+version = "1.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "42703706b716c37f96a77aea830392ad231f44c9e9a67872fa5548707e11b11c"
+
 [[package]]
 name = "futures"
 version = "0.3.30"
@@ -1098,7 +1274,7 @@ checksum = "87750cf4b7a4c0625b1529e4c543c2182106e4dedc60a2a6455e00d212c489ac"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -1147,7 +1323,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "seq-macro",
 ]
 
@@ -1162,7 +1338,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "seq-macro",
 ]
 
@@ -1177,7 +1353,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "seq-macro",
 ]
 
@@ -1195,7 +1371,7 @@ dependencies = [
  "once_cell",
  "paste",
  "pulp",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "rayon",
  "seq-macro",
  "sysctl",
@@ -1214,7 +1390,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "rayon",
  "seq-macro",
 ]
@@ -1230,7 +1406,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "seq-macro",
 ]
 
@@ -1245,7 +1421,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "seq-macro",
 ]
 
@@ -1261,9 +1437,9 @@ dependencies = [
 
 [[package]]
 name = "getrandom"
-version = "0.2.14"
+version = "0.2.15"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "94b22e06ecb0110981051723910cbf0b5f5e09a2062dd7663334ee79a9d1286c"
+checksum = "c4567c8db10ae91089c99af84c68c38da3ec2f087c3f82960bcdbf3656b6f4d7"
 dependencies = [
  "cfg-if",
  "libc",
@@ -1284,9 +1460,9 @@ dependencies = [
 
 [[package]]
 name = "gimli"
-version = "0.28.1"
+version = "0.29.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4271d37baee1b8c7e4b708028c57d816cf9d2434acb33a549475f78c181f6253"
+checksum = "40ecd4077b5ae9fd2e9e169b102c6c330d0605168eb0e8bf79952b256dbefffd"
 
 [[package]]
 name = "glob"
@@ -1323,6 +1499,25 @@ dependencies = [
  "tracing",
 ]
 
+[[package]]
+name = "h2"
+version = "0.4.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "fa82e28a107a8cc405f0839610bdc9b15f1e25ec7d696aa5cf173edbcb1486ab"
+dependencies = [
+ "atomic-waker",
+ "bytes",
+ "fnv",
+ "futures-core",
+ "futures-sink",
+ "http 1.1.0",
+ "indexmap 2.2.6",
+ "slab",
+ "tokio",
+ "tokio-util",
+ "tracing",
+]
+
 [[package]]
 name = "half"
 version = "2.4.1"
@@ -1345,19 +1540,13 @@ checksum = "8a9ee70c43aaf417c914396645a0fa852624801b24ebb7ae78fe8272889ac888"
 
 [[package]]
 name = "hashbrown"
-version = "0.13.1"
+version = "0.14.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "33ff8ae62cd3a9102e5637afc8452c55acf3844001bd5374e0b0bd7b6616c038"
+checksum = "e5274423e17b7c9fc20b6e7e208532f9b19825d82dfd615708b70edd83df41f1"
 dependencies = [
  "ahash",
 ]
 
-[[package]]
-name = "hashbrown"
-version = "0.14.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "290f1a1d9242c78d09ce40a5e87e7554ee637af1351968159f4952f028f75604"
-
 [[package]]
 name = "heck"
 version = "0.4.1"
@@ -1379,17 +1568,17 @@ checksum = "d231dfb89cfffdbc30e7fc41579ed6066ad03abda9e567ccafae602b97ec5024"
 [[package]]
 name = "hf-hub"
 version = "0.3.2"
-source = "git+https://github.com/huggingface/hf-hub?rev=b167f69692be5f49eb8003788f7f8a499a98b096#b167f69692be5f49eb8003788f7f8a499a98b096"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2b780635574b3d92f036890d8373433d6f9fc7abb320ee42a5c25897fc8ed732"
 dependencies = [
  "dirs",
  "futures",
- "http 1.1.0",
  "indicatif",
  "log",
  "native-tls",
  "num_cpus",
  "rand",
- "reqwest",
+ "reqwest 0.11.27",
  "serde",
  "serde_json",
  "thiserror",
@@ -1451,12 +1640,12 @@ dependencies = [
 
 [[package]]
 name = "http-body-util"
-version = "0.1.1"
+version = "0.1.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0475f8b2ac86659c21b64320d5d653f9efe42acd2a4e560073ec61a155a34f1d"
+checksum = "793429d76616a256bcb62c2a2ec2bed781c8307e797e2598c50010f2bee2544f"
 dependencies = [
  "bytes",
- "futures-core",
+ "futures-util",
  "http 1.1.0",
  "http-body 1.0.0",
  "pin-project-lite",
@@ -1464,9 +1653,9 @@ dependencies = [
 
 [[package]]
 name = "httparse"
-version = "1.8.0"
+version = "1.9.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d897f394bad6a705d5f4104762e116a75639e470d80901eed05a860a95cb1904"
+checksum = "0fcc0b4a115bf80b728eb8ea024ad5bd707b615bfed49e0665b6e0f86fd082d9"
 
 [[package]]
 name = "httpdate"
@@ -1476,15 +1665,15 @@ checksum = "df3b46402a9d5adb4c86a0cf463f42e19994e3ee891101b1841f30a545cb49a9"
 
 [[package]]
 name = "hyper"
-version = "0.14.28"
+version = "0.14.29"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bf96e135eb83a2a8ddf766e426a841d8ddd7449d5f00d34ea02b41d2f19eef80"
+checksum = "f361cde2f109281a220d4307746cdfd5ee3f410da58a70377762396775634b33"
 dependencies = [
  "bytes",
  "futures-channel",
  "futures-core",
  "futures-util",
- "h2",
+ "h2 0.3.26",
  "http 0.2.12",
  "http-body 0.4.6",
  "httparse",
@@ -1500,13 +1689,14 @@ dependencies = [
 
 [[package]]
 name = "hyper"
-version = "1.3.1"
+version = "1.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fe575dd17d0862a9a33781c8c4696a55c320909004a67a00fb286ba8b1bc496d"
+checksum = "c4fe55fb7a772d59a5ff1dfbff4fe0258d19b89fec4b233e75d35d5d2316badc"
 dependencies = [
  "bytes",
  "futures-channel",
  "futures-util",
+ "h2 0.4.5",
  "http 1.1.0",
  "http-body 1.0.0",
  "httparse",
@@ -1515,6 +1705,27 @@ dependencies = [
  "pin-project-lite",
  "smallvec",
  "tokio",
+ "want",
+]
+
+[[package]]
+name = "hyper-rustls"
+version = "0.27.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5ee4be2c948921a1a5320b629c4193916ed787a7f7f293fd3f7f5a6c9de74155"
+dependencies = [
+ "futures-util",
+ "http 1.1.0",
+ "hyper 1.4.0",
+ "hyper-util",
+ "log",
+ "rustls 0.23.10",
+ "rustls-native-certs",
+ "rustls-pki-types",
+ "tokio",
+ "tokio-rustls",
+ "tower-service",
+ "webpki-roots",
 ]
 
 [[package]]
@@ -1523,7 +1734,7 @@ version = "0.4.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "bbb958482e8c7be4bc3cf272a766a2b0bf1a6755e7a6ae777f017a31d11b13b1"
 dependencies = [
- "hyper 0.14.28",
+ "hyper 0.14.29",
  "pin-project-lite",
  "tokio",
  "tokio-io-timeout",
@@ -1536,26 +1747,46 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d6183ddfa99b85da61a140bea0efc93fdf56ceaa041b37d553518030827f9905"
 dependencies = [
  "bytes",
- "hyper 0.14.28",
+ "hyper 0.14.29",
+ "native-tls",
+ "tokio",
+ "tokio-native-tls",
+]
+
+[[package]]
+name = "hyper-tls"
+version = "0.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "70206fc6890eaca9fde8a0bf71caa2ddfc9fe045ac9e5c70df101a7dbde866e0"
+dependencies = [
+ "bytes",
+ "http-body-util",
+ "hyper 1.4.0",
+ "hyper-util",
  "native-tls",
  "tokio",
  "tokio-native-tls",
+ "tower-service",
 ]
 
 [[package]]
 name = "hyper-util"
-version = "0.1.3"
+version = "0.1.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ca38ef113da30126bbff9cd1705f9273e15d45498615d138b0c20279ac7a76aa"
+checksum = "3ab92f4f49ee4fb4f997c784b7a2e0fa70050211e0b6a287f898c3c9785ca956"
 dependencies = [
  "bytes",
+ "futures-channel",
  "futures-util",
  "http 1.1.0",
  "http-body 1.0.0",
- "hyper 1.3.1",
+ "hyper 1.4.0",
  "pin-project-lite",
  "socket2",
  "tokio",
+ "tower",
+ "tower-service",
+ "tracing",
 ]
 
 [[package]]
@@ -1614,7 +1845,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "168fb715dda47215e360912c096649d23d58bf392ac62f73919e831745e40f26"
 dependencies = [
  "equivalent",
- "hashbrown 0.14.3",
+ "hashbrown 0.14.5",
  "serde",
 ]
 
@@ -1633,15 +1864,16 @@ dependencies = [
 
 [[package]]
 name = "init-tracing-opentelemetry"
-version = "0.14.1"
+version = "0.18.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "94bd26b1b737bc11f183620072e188d1c6ede67e0e78682228d66b49ec510e17"
+checksum = "754367a7e7f9314afe3ee9780ba8b8ea2cab487cdc8017c9794793051242f878"
 dependencies = [
- "opentelemetry 0.20.0",
- "opentelemetry-otlp",
+ "opentelemetry 0.22.0",
+ "opentelemetry-otlp 0.15.0",
+ "opentelemetry_sdk 0.22.1",
  "thiserror",
  "tracing",
- "tracing-opentelemetry 0.21.0",
+ "tracing-opentelemetry 0.23.0",
 ]
 
 [[package]]
@@ -1660,9 +1892,9 @@ dependencies = [
 
 [[package]]
 name = "instant"
-version = "0.1.12"
+version = "0.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7a5bbe824c507c5da5956355e86a746d82e0e1464f65d862cc5e71da70e94b2c"
+checksum = "e0242819d153cba4b4b05a5a8f2a7e9bbf97b6055b2a002b395c96b5ff3c0222"
 dependencies = [
  "cfg-if",
 ]
@@ -1704,6 +1936,12 @@ dependencies = [
  "num-traits",
 ]
 
+[[package]]
+name = "is_terminal_polyfill"
+version = "1.70.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f8478577c03552c21db0e2724ffb8986a5ce7af88107e6be5d2ee6e158c12800"
+
 [[package]]
 name = "itertools"
 version = "0.10.5"
@@ -1737,6 +1975,15 @@ version = "1.0.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b"
 
+[[package]]
+name = "jobserver"
+version = "0.1.31"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d2b099aaa34a9751c5bf0878add70444e1ed2dd73f347be99003d4577277de6e"
+dependencies = [
+ "libc",
+]
+
 [[package]]
 name = "js-sys"
 version = "0.3.69"
@@ -1748,15 +1995,31 @@ dependencies = [
 
 [[package]]
 name = "lazy_static"
-version = "1.4.0"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
+
+[[package]]
+name = "lazycell"
+version = "1.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e2abad23fbc42b3700f2f279844dc832adb2b2eb069b2df918f455c4e18cc646"
+checksum = "830d08ce1d1d941e6b30645f1a0eb5643013d835ce3779a5fc208261dbe10f55"
 
 [[package]]
 name = "libc"
-version = "0.2.153"
+version = "0.2.155"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9c198f91728a82281a64e1f4f9eeb25d82cb32a5de251c6bd1b5154d63a8e7bd"
+checksum = "97b3888a4aecf77e811145cadf6eef5901f4782c53886191b2f693f24761847c"
+
+[[package]]
+name = "libloading"
+version = "0.8.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e310b3a6b5907f99202fcdb4960ff45b93735d7c7d96b760fcff8db2dc0e103d"
+dependencies = [
+ "cfg-if",
+ "windows-targets 0.52.5",
+]
 
 [[package]]
 name = "libm"
@@ -1766,9 +2029,9 @@ checksum = "4ec2a862134d2a7d32d7983ddcdd1c4923530833c9f2ea1a44fc5fa473989058"
 
 [[package]]
 name = "libmimalloc-sys"
-version = "0.1.37"
+version = "0.1.39"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "81eb4061c0582dedea1cbc7aff2240300dd6982e0239d1c99e65c1dbf4a30ba7"
+checksum = "23aa6811d3bd4deb8a84dde645f943476d13b248d818edcf8ce0b2f37f036b44"
 dependencies = [
  "cc",
  "libc",
@@ -1780,7 +2043,7 @@ version = "0.1.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c0ff37bd590ca25063e35af745c343cb7a0271906fb7b37e4813e8f79f00268d"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "libc",
 ]
 
@@ -1792,15 +2055,15 @@ checksum = "0717cef1bc8b636c6e1c1bbdefc09e6322da8a9321966e8928ef80d20f7f770f"
 
 [[package]]
 name = "linux-raw-sys"
-version = "0.4.13"
+version = "0.4.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "01cda141df6706de531b6c46c3a33ecca755538219bd484262fa09410c13539c"
+checksum = "78b3ae25bc7c8c38cec158d1f2757ee79e9b3740fbc7ccf0e59e4b08d793fa89"
 
 [[package]]
 name = "lock_api"
-version = "0.4.11"
+version = "0.4.12"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3c168f8615b12bc01f9c17e2eb0cc07dcae1940121185446edc3744920e8ef45"
+checksum = "07af8b9cdd281b7915f413fa73f29ebd5d55d0d3f0155584dade1ff18cea1b17"
 dependencies = [
  "autocfg",
  "scopeguard",
@@ -1808,18 +2071,9 @@ dependencies = [
 
 [[package]]
 name = "log"
-version = "0.4.21"
+version = "0.4.22"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "90ed8c1e510134f979dbc4f070f87d4313098b704861a105fe34231c70a3901c"
-
-[[package]]
-name = "mach2"
-version = "0.4.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "19b955cdeb2a02b9117f121ce63aa52d08ade45de53e48fe6a38b39c10f6f709"
-dependencies = [
- "libc",
-]
+checksum = "a7a70ba024b9dc04c27ea2f0c0548feb474ec5c54bba33a7f72f873a39d07b24"
 
 [[package]]
 name = "macro_rules_attribute"
@@ -1861,11 +2115,21 @@ version = "0.7.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0e7465ac9959cc2b1404e8e2367b43684a6d13790fe23056cc8c6c5a6b7bcb94"
 
+[[package]]
+name = "matrixmultiply"
+version = "0.3.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7574c1cf36da4798ab73da5b215bbf444f50718207754cb522201d78d1cd0ff2"
+dependencies = [
+ "autocfg",
+ "rawpointer",
+]
+
 [[package]]
 name = "memchr"
-version = "2.7.2"
+version = "2.7.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6c8640c5d730cb13ebd907d8d04b52f55ac9a2eec55b440c8892f40d56c76c1d"
+checksum = "78ca9ab1a0babb1e7d5695e3530886289c18cf2f87ec19a575a0abdce112e3a3"
 
 [[package]]
 name = "memmap2"
@@ -1883,7 +2147,7 @@ version = "0.27.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c43f73953f8cbe511f021b58f18c3ce1c3d1ae13fe953293e13345bf83217f25"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "block",
  "core-graphics-types",
  "foreign-types 0.5.0",
@@ -1894,24 +2158,26 @@ dependencies = [
 
 [[package]]
 name = "metrics"
-version = "0.21.1"
+version = "0.23.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fde3af1a009ed76a778cb84fdef9e7dbbdf5775ae3e4cc1f434a6a307f6f76c5"
+checksum = "884adb57038347dfbaf2d5065887b6cf4312330dc8e94bc30a1a839bd79d3261"
 dependencies = [
  "ahash",
- "metrics-macros",
  "portable-atomic",
 ]
 
 [[package]]
 name = "metrics-exporter-prometheus"
-version = "0.12.2"
+version = "0.15.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1d4fa7ce7c4862db464a37b0b31d89bca874562f034bd7993895572783d02950"
+checksum = "bf0af7a0d7ced10c0151f870e5e3f3f8bc9ffc5992d32873566ca1f9169ae776"
 dependencies = [
- "base64 0.21.7",
- "hyper 0.14.28",
- "indexmap 1.9.3",
+ "base64 0.22.1",
+ "http-body-util",
+ "hyper 1.4.0",
+ "hyper-rustls",
+ "hyper-util",
+ "indexmap 2.2.6",
  "ipnet",
  "metrics",
  "metrics-util",
@@ -1921,26 +2187,15 @@ dependencies = [
  "tracing",
 ]
 
-[[package]]
-name = "metrics-macros"
-version = "0.7.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "38b4faf00617defe497754acde3024865bc143d44a86799b24e191ecff91354f"
-dependencies = [
- "proc-macro2",
- "quote",
- "syn 2.0.60",
-]
-
 [[package]]
 name = "metrics-util"
-version = "0.15.1"
+version = "0.17.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4de2ed6e491ed114b40b732e4d1659a9d53992ebd87490c44a6ffe23739d973e"
+checksum = "4259040465c955f9f2f1a4a8a16dc46726169bca0f88e8fb2dbeced487c3e828"
 dependencies = [
  "crossbeam-epoch",
  "crossbeam-utils",
- "hashbrown 0.13.1",
+ "hashbrown 0.14.5",
  "metrics",
  "num_cpus",
  "quanta",
@@ -1949,9 +2204,9 @@ dependencies = [
 
 [[package]]
 name = "mimalloc"
-version = "0.1.41"
+version = "0.1.43"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9f41a2280ded0da56c8cf898babb86e8f10651a34adcfff190ae9a1159c6908d"
+checksum = "68914350ae34959d83f732418d51e2427a794055d0b9529f48259ac07af65633"
 dependencies = [
  "libmimalloc-sys",
 ]
@@ -1964,9 +2219,9 @@ checksum = "6877bb514081ee2a7ff5ef9de3281f14a4dd4bceac4c09388074a6b5df8a139a"
 
 [[package]]
 name = "mime_guess"
-version = "2.0.4"
+version = "2.0.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4192263c238a5f0d0c6bfd21f336a313a4ce1c450542449ca191bb657b4642ef"
+checksum = "f7c44f8e672c00fe5308fa235f821cb4198414e1c77935c1ab6948d3fd78550e"
 dependencies = [
  "mime",
  "unicase",
@@ -1980,9 +2235,9 @@ checksum = "68354c5c6bd36d73ff3feceb05efa59b6acb7626617f4962be322a825e61f79a"
 
 [[package]]
 name = "miniz_oxide"
-version = "0.7.2"
+version = "0.7.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9d811f3e15f28568be3407c8e7fdb6514c1cda3cb30683f15b6a1a1dc4ea14a7"
+checksum = "b8a240ddb74feaf34a79a7add65a741f3167852fba007066dcac1ca548d89c08"
 dependencies = [
  "adler",
 ]
@@ -1998,11 +2253,17 @@ dependencies = [
  "windows-sys 0.48.0",
 ]
 
+[[package]]
+name = "mirai-annotations"
+version = "1.12.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c9be0862c1b3f26a88803c4a49de6889c10e608b3ee9344e6ef5b45fb37ad3d1"
+
 [[package]]
 name = "monostate"
-version = "0.1.12"
+version = "0.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a20fffcd8ca4c69d31e036a71abc400147b41f90895df4edcb36497a1f8af8bf"
+checksum = "0d208407d7552cd041d8cdb69a1bc3303e029c598738177a3d87082004dc0e1e"
 dependencies = [
  "monostate-impl",
  "serde",
@@ -2010,13 +2271,13 @@ dependencies = [
 
 [[package]]
 name = "monostate-impl"
-version = "0.1.12"
+version = "0.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bf307cbbbd777a9c10cec88ddafee572b3484caad5cce0c9236523c3803105a6"
+checksum = "a7ce64b975ed4f123575d11afd9491f2e37bbd5813fbfbc0f09ae1fbddea74e0"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -2033,11 +2294,10 @@ checksum = "defc4c55412d89136f966bbb339008b474350e5e6e78d2714439c386b3137a03"
 
 [[package]]
 name = "native-tls"
-version = "0.2.11"
+version = "0.2.12"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "07226173c32f2926027b63cce4bcd8076c3552846cbe7925f3aaffeac0a3b92e"
+checksum = "a8614eb2c83d59d1c8cc974dd3f920198647674a0a035e1af1fa58707e317466"
 dependencies = [
- "lazy_static",
  "libc",
  "log",
  "openssl",
@@ -2049,6 +2309,21 @@ dependencies = [
  "tempfile",
 ]
 
+[[package]]
+name = "ndarray"
+version = "0.16.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "882ed72dce9365842bf196bdeedf5055305f11fc8c03dee7bb0194a6cad34841"
+dependencies = [
+ "matrixmultiply",
+ "num-complex",
+ "num-integer",
+ "num-traits",
+ "portable-atomic",
+ "portable-atomic-util",
+ "rawpointer",
+]
+
 [[package]]
 name = "nohash-hasher"
 version = "0.2.0"
@@ -2077,9 +2352,9 @@ dependencies = [
 
 [[package]]
 name = "num-complex"
-version = "0.4.5"
+version = "0.4.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "23c6602fda94a57c990fe0df199a035d83576b496aa29f4e634a8ac6004e68a6"
+checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495"
 dependencies = [
  "bytemuck",
  "num-traits",
@@ -2091,11 +2366,20 @@ version = "0.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "51d515d32fb182ee37cda2ccdcb92950d6a3c2893aa280e540671c2cd0f3b1d9"
 
+[[package]]
+name = "num-integer"
+version = "0.1.46"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7969661fd2958a5cb096e56c8e1ad0444ac2bbcd0061bd28660485a44879858f"
+dependencies = [
+ "num-traits",
+]
+
 [[package]]
 name = "num-traits"
-version = "0.2.18"
+version = "0.2.19"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "da0df0e5185db44f69b44f26786fe401b6c293d1907744beaa7fa62b2e5a517a"
+checksum = "071dfc062690e90b734c0b2273ce72ad0ffa95f0c74596bc250dcfd960262841"
 dependencies = [
  "autocfg",
  "libm",
@@ -2111,6 +2395,27 @@ dependencies = [
  "libc",
 ]
 
+[[package]]
+name = "num_enum"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "02339744ee7253741199f897151b38e72257d13802d4ee837285cc2990a90845"
+dependencies = [
+ "num_enum_derive",
+]
+
+[[package]]
+name = "num_enum_derive"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "681030a937600a36906c185595136d26abfebb4aa9c65701cefcaf8578bb982b"
+dependencies = [
+ "proc-macro-crate",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.68",
+]
+
 [[package]]
 name = "num_threads"
 version = "0.1.7"
@@ -2147,23 +2452,25 @@ dependencies = [
 
 [[package]]
 name = "object"
-version = "0.32.2"
+version = "0.36.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a6a622008b6e321afc04970976f62ee297fdbaa6f95318ca343e3eebb9648441"
+checksum = "081b846d1d56ddfc18fdf1a922e4f6e07a11768ea1b92dec44e42b72712ccfce"
 dependencies = [
  "memchr",
 ]
 
 [[package]]
 name = "oci-spec"
-version = "0.6.5"
+version = "0.6.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e423c4f827362c0d8d8da4b1f571270f389ebde73bcd3240a3d23c6d6f61d0f0"
+checksum = "098bc31d7c87110e22090eb082903caaed19f06319f63b6f7d7ed273bc8bdaec"
 dependencies = [
  "derive_builder",
  "getset",
  "serde",
  "serde_json",
+ "strum",
+ "strum_macros",
  "thiserror",
 ]
 
@@ -2174,7 +2481,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9bb3293021f06540803301af45e7ab81693d50e89a7398a3420bdab139e7ba5e"
 dependencies = [
  "base16ct",
- "base64 0.22.0",
+ "base64 0.22.1",
  "chrono",
  "directories",
  "flate2",
@@ -2228,7 +2535,7 @@ version = "0.10.64"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "95a0481286a310808298130d22dd1fef0fa571e05a8f44ec801801e84b216b1f"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "cfg-if",
  "foreign-types 0.3.2",
  "libc",
@@ -2245,7 +2552,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -2272,102 +2579,116 @@ version = "0.19.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "5f4b8347cc26099d3aeee044065ecc3ae11469796b4d65d065a23a584ed92a6f"
 dependencies = [
- "opentelemetry_api 0.19.0",
+ "opentelemetry_api",
  "opentelemetry_sdk 0.19.0",
 ]
 
 [[package]]
 name = "opentelemetry"
-version = "0.20.0"
+version = "0.22.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9591d937bc0e6d2feb6f71a559540ab300ea49955229c347a517a28d27784c54"
+checksum = "900d57987be3f2aeb70d385fff9b27fb74c5723cc9a52d904d4f9c807a0667bf"
 dependencies = [
- "opentelemetry_api 0.20.0",
- "opentelemetry_sdk 0.20.0",
+ "futures-core",
+ "futures-sink",
+ "js-sys",
+ "once_cell",
+ "pin-project-lite",
+ "thiserror",
+ "urlencoding",
 ]
 
 [[package]]
 name = "opentelemetry"
-version = "0.21.0"
+version = "0.23.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1e32339a5dc40459130b3bd269e9892439f55b33e772d2a9d402a789baaf4e8a"
+checksum = "1b69a91d4893e713e06f724597ad630f1fa76057a5e1026c0ca67054a9032a76"
 dependencies = [
  "futures-core",
  "futures-sink",
- "indexmap 2.2.6",
  "js-sys",
  "once_cell",
  "pin-project-lite",
  "thiserror",
- "urlencoding",
 ]
 
 [[package]]
 name = "opentelemetry-otlp"
-version = "0.13.0"
+version = "0.15.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7e5e5a5c4135864099f3faafbe939eb4d7f9b80ebf68a8448da961b32a7c1275"
+checksum = "1a016b8d9495c639af2145ac22387dcb88e44118e45320d9238fbf4e7889abcb"
 dependencies = [
  "async-trait",
  "futures-core",
  "http 0.2.12",
- "opentelemetry-proto",
+ "opentelemetry 0.22.0",
+ "opentelemetry-proto 0.5.0",
  "opentelemetry-semantic-conventions",
- "opentelemetry_api 0.20.0",
- "opentelemetry_sdk 0.20.0",
- "prost 0.11.9",
+ "opentelemetry_sdk 0.22.1",
+ "prost 0.12.6",
  "thiserror",
  "tokio",
- "tonic 0.9.2",
+ "tonic 0.11.0",
 ]
 
 [[package]]
-name = "opentelemetry-proto"
-version = "0.3.0"
+name = "opentelemetry-otlp"
+version = "0.16.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b1e3f814aa9f8c905d0ee4bde026afd3b2577a97c10e1699912e3e44f0c4cbeb"
+checksum = "a94c69209c05319cdf7460c6d4c055ed102be242a0a6245835d7bc42c6ec7f54"
 dependencies = [
- "opentelemetry_api 0.20.0",
- "opentelemetry_sdk 0.20.0",
- "prost 0.11.9",
- "tonic 0.9.2",
+ "async-trait",
+ "futures-core",
+ "http 0.2.12",
+ "opentelemetry 0.23.0",
+ "opentelemetry-proto 0.6.0",
+ "opentelemetry_sdk 0.23.0",
+ "prost 0.12.6",
+ "thiserror",
+ "tokio",
+ "tonic 0.11.0",
 ]
 
 [[package]]
-name = "opentelemetry-semantic-conventions"
-version = "0.12.0"
+name = "opentelemetry-proto"
+version = "0.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "73c9f9340ad135068800e7f1b24e9e09ed9e7143f5bf8518ded3d3ec69789269"
+checksum = "3a8fddc9b68f5b80dae9d6f510b88e02396f006ad48cac349411fbecc80caae4"
 dependencies = [
- "opentelemetry 0.20.0",
+ "opentelemetry 0.22.0",
+ "opentelemetry_sdk 0.22.1",
+ "prost 0.12.6",
+ "tonic 0.11.0",
 ]
 
 [[package]]
-name = "opentelemetry_api"
-version = "0.19.0"
+name = "opentelemetry-proto"
+version = "0.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ed41783a5bf567688eb38372f2b7a8530f5a607a4b49d38dd7573236c23ca7e2"
+checksum = "984806e6cf27f2b49282e2a05e288f30594f3dbc74eb7a6e99422bc48ed78162"
 dependencies = [
- "fnv",
- "futures-channel",
- "futures-util",
- "indexmap 1.9.3",
- "once_cell",
- "pin-project-lite",
- "thiserror",
- "urlencoding",
+ "opentelemetry 0.23.0",
+ "opentelemetry_sdk 0.23.0",
+ "prost 0.12.6",
+ "tonic 0.11.0",
 ]
 
+[[package]]
+name = "opentelemetry-semantic-conventions"
+version = "0.14.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f9ab5bd6c42fb9349dcf28af2ba9a0667f697f9bdcca045d39f2cec5543e2910"
+
 [[package]]
 name = "opentelemetry_api"
-version = "0.20.0"
+version = "0.19.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8a81f725323db1b1206ca3da8bb19874bbd3f57c3bcd59471bfb04525b265b9b"
+checksum = "ed41783a5bf567688eb38372f2b7a8530f5a607a4b49d38dd7573236c23ca7e2"
 dependencies = [
+ "fnv",
  "futures-channel",
  "futures-util",
  "indexmap 1.9.3",
- "js-sys",
  "once_cell",
  "pin-project-lite",
  "thiserror",
@@ -2388,7 +2709,7 @@ dependencies = [
  "futures-executor",
  "futures-util",
  "once_cell",
- "opentelemetry_api 0.19.0",
+ "opentelemetry_api",
  "percent-encoding",
  "rand",
  "thiserror",
@@ -2396,22 +2717,21 @@ dependencies = [
 
 [[package]]
 name = "opentelemetry_sdk"
-version = "0.20.0"
+version = "0.22.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fa8e705a0612d48139799fcbaba0d4a90f06277153e43dd2bdc16c6f0edd8026"
+checksum = "9e90c7113be649e31e9a0f8b5ee24ed7a16923b322c3c5ab6367469c049d6b7e"
 dependencies = [
  "async-trait",
  "crossbeam-channel",
  "futures-channel",
  "futures-executor",
  "futures-util",
+ "glob",
  "once_cell",
- "opentelemetry_api 0.20.0",
- "ordered-float 3.9.2",
+ "opentelemetry 0.22.0",
+ "ordered-float",
  "percent-encoding",
  "rand",
- "regex",
- "serde_json",
  "thiserror",
  "tokio",
  "tokio-stream",
@@ -2419,22 +2739,24 @@ dependencies = [
 
 [[package]]
 name = "opentelemetry_sdk"
-version = "0.21.2"
+version = "0.23.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2f16aec8a98a457a52664d69e0091bac3a0abd18ead9b641cb00202ba4e0efe4"
+checksum = "ae312d58eaa90a82d2e627fd86e075cf5230b3f11794e2ed74199ebbe572d4fd"
 dependencies = [
  "async-trait",
- "crossbeam-channel",
  "futures-channel",
  "futures-executor",
  "futures-util",
  "glob",
+ "lazy_static",
  "once_cell",
- "opentelemetry 0.21.0",
- "ordered-float 4.2.0",
+ "opentelemetry 0.23.0",
+ "ordered-float",
  "percent-encoding",
  "rand",
  "thiserror",
+ "tokio",
+ "tokio-stream",
 ]
 
 [[package]]
@@ -2445,20 +2767,36 @@ checksum = "04744f49eae99ab78e0d5c0b603ab218f515ea8cfe5a456d7629ad883a3b6e7d"
 
 [[package]]
 name = "ordered-float"
-version = "3.9.2"
+version = "4.2.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f1e1c390732d15f1d48471625cd92d154e66db2c56645e29a9cd26f4699f72dc"
+checksum = "19ff2cf528c6c03d9ed653d6c4ce1dc0582dc4af309790ad92f07c1cd551b0be"
 dependencies = [
  "num-traits",
 ]
 
 [[package]]
-name = "ordered-float"
-version = "4.2.0"
+name = "ort"
+version = "2.0.0-rc.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a76df7075c7d4d01fdcb46c912dd17fba5b60c78ea480b475f2b6ab6f666584e"
+checksum = "11826e6118cc42fea0cb2b102f7d006c1bb339cb167f8badb5fb568616438234"
 dependencies = [
- "num-traits",
+ "half",
+ "ndarray",
+ "ort-sys",
+ "tracing",
+]
+
+[[package]]
+name = "ort-sys"
+version = "2.0.0-rc.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c4780a8b8681e653b2bed85c7f0e2c6e8547224c3e983e5ad27bf0457e012407"
+dependencies = [
+ "flate2",
+ "pkg-config",
+ "sha2",
+ "tar",
+ "ureq",
 ]
 
 [[package]]
@@ -2467,11 +2805,17 @@ version = "0.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b15813163c1d831bf4a13c3610c05c0d03b39feb07f7e09fa234dac9b15aaf39"
 
+[[package]]
+name = "parking"
+version = "2.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bb813b8af86854136c6922af0598d719255ecb2179515e6e7730d468f05c9cae"
+
 [[package]]
 name = "parking_lot"
-version = "0.12.1"
+version = "0.12.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3742b2c103b9f06bc9fff0a37ff4912935851bee6d36f3c02bcc755bcfec228f"
+checksum = "f1bf18183cf54e8d6059647fc3063646a1801cf30896933ec2311622cc4b9a27"
 dependencies = [
  "lock_api",
  "parking_lot_core",
@@ -2479,22 +2823,22 @@ dependencies = [
 
 [[package]]
 name = "parking_lot_core"
-version = "0.9.9"
+version = "0.9.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4c42a9226546d68acdd9c0a280d17ce19bfe27a46bf68784e4066115788d008e"
+checksum = "1e401f977ab385c9e4e3ab30627d6f26d00e2c73eef317493c4ec6d468726cf8"
 dependencies = [
  "cfg-if",
  "libc",
- "redox_syscall",
+ "redox_syscall 0.5.2",
  "smallvec",
- "windows-targets 0.48.5",
+ "windows-targets 0.52.5",
 ]
 
 [[package]]
 name = "paste"
-version = "1.0.14"
+version = "1.0.15"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "de3145af08024dea9fa9914f381a17b8fc6034dfb00f3a84013f7ff43f29ed4c"
+checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a"
 
 [[package]]
 name = "percent-encoding"
@@ -2504,9 +2848,9 @@ checksum = "e3148f5046208a5d56bcfc03053e3ca6334e51da8dfb19b6cdc8b306fae3283e"
 
 [[package]]
 name = "petgraph"
-version = "0.6.4"
+version = "0.6.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e1d3afd2628e69da2be385eb6f2fd57c8ac7977ceeff6dc166ff1657b0e386a9"
+checksum = "b4c5cc86750666a3ed20bdaf5ca2a0344f9c67674cae0515bec2da16fbaa47db"
 dependencies = [
  "fixedbitset",
  "indexmap 2.2.6",
@@ -2529,7 +2873,7 @@ checksum = "2f38a4412a78282e09a2cf38d195ea5420d15ba0602cb375210efbc877243965"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -2556,6 +2900,15 @@ version = "1.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7170ef9988bc169ba16dd36a7fa041e5c4cbeb6a35b76d4c03daded371eae7c0"
 
+[[package]]
+name = "portable-atomic-util"
+version = "0.2.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "90a7d5beecc52a491b54d6dd05c7a45ba1801666a5baad9fdbfc6fef8d2d206c"
+dependencies = [
+ "portable-atomic",
+]
+
 [[package]]
 name = "powerfmt"
 version = "0.2.0"
@@ -2580,12 +2933,21 @@ dependencies = [
 
 [[package]]
 name = "prettyplease"
-version = "0.2.19"
+version = "0.2.20"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5ac2cf0f2e4f42b49f5ffd07dae8d746508ef7526c13940e5f524012ae6c6550"
+checksum = "5f12335488a2f3b0a83b14edad48dca9879ce89b2edd10e80237e4e852dd645e"
 dependencies = [
  "proc-macro2",
- "syn 2.0.60",
+ "syn 2.0.68",
+]
+
+[[package]]
+name = "proc-macro-crate"
+version = "3.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6d37c51ca738a55da99dc0c4a34860fd675453b8b36209178c2249bb13651284"
+dependencies = [
+ "toml_edit 0.21.1",
 ]
 
 [[package]]
@@ -2614,9 +2976,9 @@ dependencies = [
 
 [[package]]
 name = "proc-macro2"
-version = "1.0.81"
+version = "1.0.86"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3d1597b0c024618f09a9c3b8655b7e430397a36d23fdafec26d6965e9eec3eba"
+checksum = "5e719e8df665df0d1c8fbfd238015744736151d4445ec0836b8e628aae103b77"
 dependencies = [
  "unicode-ident",
 ]
@@ -2633,12 +2995,12 @@ dependencies = [
 
 [[package]]
 name = "prost"
-version = "0.12.4"
+version = "0.12.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d0f5d036824e4761737860779c906171497f6d55681139d8312388f8fe398922"
+checksum = "deb1435c188b76130da55f17a466d252ff7b1418b2ad3e037d127b94e3411f29"
 dependencies = [
  "bytes",
- "prost-derive 0.12.4",
+ "prost-derive 0.12.6",
 ]
 
 [[package]]
@@ -2665,9 +3027,9 @@ dependencies = [
 
 [[package]]
 name = "prost-build"
-version = "0.12.4"
+version = "0.12.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "80b776a1b2dc779f5ee0641f8ade0125bc1298dd41a9a0c16d8bd57b42d222b1"
+checksum = "22505a5c94da8e3b7c2996394d1c933236c4d743e81a410bcca4e6989fc066a4"
 dependencies = [
  "bytes",
  "heck 0.5.0",
@@ -2676,11 +3038,11 @@ dependencies = [
  "multimap 0.10.0",
  "once_cell",
  "petgraph",
- "prettyplease 0.2.19",
- "prost 0.12.4",
- "prost-types 0.12.4",
+ "prettyplease 0.2.20",
+ "prost 0.12.6",
+ "prost-types 0.12.6",
  "regex",
- "syn 2.0.60",
+ "syn 2.0.68",
  "tempfile",
 ]
 
@@ -2699,15 +3061,15 @@ dependencies = [
 
 [[package]]
 name = "prost-derive"
-version = "0.12.4"
+version = "0.12.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "19de2de2a00075bf566bee3bd4db014b11587e84184d3f7a791bc17f1a8e9e48"
+checksum = "81bddcdb20abf9501610992b6759a4c888aef7d1a7247ef75e2404275ac24af1"
 dependencies = [
  "anyhow",
  "itertools 0.12.1",
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -2720,40 +3082,86 @@ dependencies = [
 ]
 
 [[package]]
-name = "prost-types"
-version = "0.12.4"
+name = "prost-types"
+version = "0.12.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9091c90b0a32608e984ff2fa4091273cbdd755d54935c51d520887f4a1dbd5b0"
+dependencies = [
+ "prost 0.12.6",
+]
+
+[[package]]
+name = "pulp"
+version = "0.18.21"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0ec8d02258294f59e4e223b41ad7e81c874aa6b15bc4ced9ba3965826da0eed5"
+dependencies = [
+ "bytemuck",
+ "libm",
+ "num-complex",
+ "reborrow",
+]
+
+[[package]]
+name = "quanta"
+version = "0.12.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8e5167a477619228a0b284fac2674e3c388cba90631d7b7de620e6f1fcd08da5"
+dependencies = [
+ "crossbeam-utils",
+ "libc",
+ "once_cell",
+ "raw-cpuid 11.0.2",
+ "wasi",
+ "web-sys",
+ "winapi",
+]
+
+[[package]]
+name = "quinn"
+version = "0.11.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3235c33eb02c1f1e212abdbe34c78b264b038fb58ca612664343271e36e55ffe"
+checksum = "e4ceeeeabace7857413798eb1ffa1e9c905a9946a57d81fb69b4b71c4d8eb3ad"
 dependencies = [
- "prost 0.12.4",
+ "bytes",
+ "pin-project-lite",
+ "quinn-proto",
+ "quinn-udp",
+ "rustc-hash",
+ "rustls 0.23.10",
+ "thiserror",
+ "tokio",
+ "tracing",
 ]
 
 [[package]]
-name = "pulp"
-version = "0.18.10"
+name = "quinn-proto"
+version = "0.11.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e14989307e408d9f4245d4fda09a7b144a08114ba124e26cab60ab83dc98db10"
+checksum = "ddf517c03a109db8100448a4be38d498df8a210a99fe0e1b9eaf39e78c640efe"
 dependencies = [
- "bytemuck",
- "libm",
- "num-complex",
- "reborrow",
+ "bytes",
+ "rand",
+ "ring",
+ "rustc-hash",
+ "rustls 0.23.10",
+ "slab",
+ "thiserror",
+ "tinyvec",
+ "tracing",
 ]
 
 [[package]]
-name = "quanta"
-version = "0.11.1"
+name = "quinn-udp"
+version = "0.5.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a17e662a7a8291a865152364c20c7abc5e60486ab2001e8ec10b24862de0b9ab"
+checksum = "9096629c45860fc7fb143e125eb826b5e721e10be3263160c7d60ca832cf8c46"
 dependencies = [
- "crossbeam-utils",
  "libc",
- "mach2",
  "once_cell",
- "raw-cpuid",
- "wasi",
- "web-sys",
- "winapi",
+ "socket2",
+ "tracing",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -2814,6 +3222,21 @@ dependencies = [
  "bitflags 1.3.2",
 ]
 
+[[package]]
+name = "raw-cpuid"
+version = "11.0.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e29830cbb1290e404f24c73af91c5d8d631ce7e128691e9477556b540cd01ecd"
+dependencies = [
+ "bitflags 2.6.0",
+]
+
+[[package]]
+name = "rawpointer"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "60a357793950651c4ed0f3f52338f53b2f809f32d83a07f72909fa13e4c6c1e3"
+
 [[package]]
 name = "rayon"
 version = "1.10.0"
@@ -2860,6 +3283,15 @@ dependencies = [
  "bitflags 1.3.2",
 ]
 
+[[package]]
+name = "redox_syscall"
+version = "0.5.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c82cf8cff14456045f55ec4241383baeff27af886adb72ffb2162f99911de0fd"
+dependencies = [
+ "bitflags 2.6.0",
+]
+
 [[package]]
 name = "redox_users"
 version = "0.4.5"
@@ -2873,14 +3305,14 @@ dependencies = [
 
 [[package]]
 name = "regex"
-version = "1.10.4"
+version = "1.10.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c117dbdfde9c8308975b6a18d71f3f385c89461f7b3fb054288ecf2a2058ba4c"
+checksum = "b91213439dad192326a0d7c6ee3955910425f441d7038e0d6933b0aec5c4517f"
 dependencies = [
  "aho-corasick",
  "memchr",
- "regex-automata 0.4.6",
- "regex-syntax 0.8.3",
+ "regex-automata 0.4.7",
+ "regex-syntax 0.8.4",
 ]
 
 [[package]]
@@ -2894,13 +3326,13 @@ dependencies = [
 
 [[package]]
 name = "regex-automata"
-version = "0.4.6"
+version = "0.4.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "86b83b8b9847f9bf95ef68afb0b8e6cdb80f498442f5179a29fad448fcc1eaea"
+checksum = "38caf58cc5ef2fed281f89292ef23f6365465ed9a41b7a7754eb4e26496c92df"
 dependencies = [
  "aho-corasick",
  "memchr",
- "regex-syntax 0.8.3",
+ "regex-syntax 0.8.4",
 ]
 
 [[package]]
@@ -2911,9 +3343,9 @@ checksum = "f162c6dd7b008981e4d40210aca20b4bd0f9b60ca9271061b07f78537722f2e1"
 
 [[package]]
 name = "regex-syntax"
-version = "0.8.3"
+version = "0.8.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "adad44e29e4c806119491a7f06f03de4d1af22c3a680dd47f1e6e179439d1f56"
+checksum = "7a66a03ae7c801facd77a29370b4faec201768915ac14a721ba36f20bc9c209b"
 
 [[package]]
 name = "reqwest"
@@ -2926,11 +3358,11 @@ dependencies = [
  "encoding_rs",
  "futures-core",
  "futures-util",
- "h2",
+ "h2 0.3.26",
  "http 0.2.12",
  "http-body 0.4.6",
- "hyper 0.14.28",
- "hyper-tls",
+ "hyper 0.14.29",
+ "hyper-tls 0.5.0",
  "ipnet",
  "js-sys",
  "log",
@@ -2939,7 +3371,7 @@ dependencies = [
  "once_cell",
  "percent-encoding",
  "pin-project-lite",
- "rustls-pemfile",
+ "rustls-pemfile 1.0.4",
  "serde",
  "serde_json",
  "serde_urlencoded",
@@ -2952,7 +3384,56 @@ dependencies = [
  "wasm-bindgen",
  "wasm-bindgen-futures",
  "web-sys",
- "winreg",
+ "winreg 0.50.0",
+]
+
+[[package]]
+name = "reqwest"
+version = "0.12.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c7d6d2a27d57148378eb5e111173f4276ad26340ecc5c49a4a2152167a2d6a37"
+dependencies = [
+ "base64 0.22.1",
+ "bytes",
+ "encoding_rs",
+ "futures-channel",
+ "futures-core",
+ "futures-util",
+ "h2 0.4.5",
+ "http 1.1.0",
+ "http-body 1.0.0",
+ "http-body-util",
+ "hyper 1.4.0",
+ "hyper-rustls",
+ "hyper-tls 0.6.0",
+ "hyper-util",
+ "ipnet",
+ "js-sys",
+ "log",
+ "mime",
+ "native-tls",
+ "once_cell",
+ "percent-encoding",
+ "pin-project-lite",
+ "quinn",
+ "rustls 0.23.10",
+ "rustls-pemfile 2.1.2",
+ "rustls-pki-types",
+ "serde",
+ "serde_json",
+ "serde_urlencoded",
+ "sync_wrapper 1.0.1",
+ "system-configuration",
+ "tokio",
+ "tokio-native-tls",
+ "tokio-rustls",
+ "tower-service",
+ "url",
+ "wasm-bindgen",
+ "wasm-bindgen-futures",
+ "web-sys",
+ "webpki-roots",
+ "winreg 0.52.0",
 ]
 
 [[package]]
@@ -2972,9 +3453,9 @@ dependencies = [
 
 [[package]]
 name = "rust-embed"
-version = "8.3.0"
+version = "8.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fb78f46d0066053d16d4ca7b898e9343bc3530f71c61d5ad84cd404ada068745"
+checksum = "19549741604902eb99a7ed0ee177a0663ee1eda51a29f71401f166e47e77806a"
 dependencies = [
  "rust-embed-impl",
  "rust-embed-utils",
@@ -2983,22 +3464,22 @@ dependencies = [
 
 [[package]]
 name = "rust-embed-impl"
-version = "8.3.0"
+version = "8.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b91ac2a3c6c0520a3fb3dd89321177c3c692937c4eb21893378219da10c44fc8"
+checksum = "cb9f96e283ec64401f30d3df8ee2aaeb2561f34c824381efa24a35f79bf40ee4"
 dependencies = [
  "proc-macro2",
  "quote",
  "rust-embed-utils",
- "syn 2.0.60",
+ "syn 2.0.68",
  "walkdir",
 ]
 
 [[package]]
 name = "rust-embed-utils"
-version = "8.3.0"
+version = "8.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "86f69089032567ffff4eada41c573fc43ff466c7db7c5688b2e7969584345581"
+checksum = "38c74a686185620830701348de757fd36bef4aa9680fd23c49fc539ddcc1af32"
 dependencies = [
  "sha2",
  "walkdir",
@@ -3006,9 +3487,15 @@ dependencies = [
 
 [[package]]
 name = "rustc-demangle"
-version = "0.1.23"
+version = "0.1.24"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "719b953e2095829ee67db738b3bfa9fa368c94900df327b3f07fe6e794d2fe1f"
+
+[[package]]
+name = "rustc-hash"
+version = "1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d626bb9dae77e28219937af045c257c28bfd3f69333c512553507f5f9798cb76"
+checksum = "08d43f7aa6b08d49f382cde6a7982047c3426db949b1424bc4b7ec9ae12c6ce2"
 
 [[package]]
 name = "rustix"
@@ -3016,7 +3503,7 @@ version = "0.38.34"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "70dc5ec042f7a43c4a73241207cecc9873a06d45debb38b329f8541d85c2730f"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "errno",
  "libc",
  "linux-raw-sys",
@@ -3037,6 +3524,35 @@ dependencies = [
  "zeroize",
 ]
 
+[[package]]
+name = "rustls"
+version = "0.23.10"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "05cff451f60db80f490f3c182b77c35260baace73209e9cdbbe526bfe3a4d402"
+dependencies = [
+ "aws-lc-rs",
+ "log",
+ "once_cell",
+ "ring",
+ "rustls-pki-types",
+ "rustls-webpki",
+ "subtle",
+ "zeroize",
+]
+
+[[package]]
+name = "rustls-native-certs"
+version = "0.7.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8f1fb85efa936c42c6d5fc28d2629bb51e4b2f4b8a5211e297d599cc5a093792"
+dependencies = [
+ "openssl-probe",
+ "rustls-pemfile 2.1.2",
+ "rustls-pki-types",
+ "schannel",
+ "security-framework",
+]
+
 [[package]]
 name = "rustls-pemfile"
 version = "1.0.4"
@@ -3046,18 +3562,29 @@ dependencies = [
  "base64 0.21.7",
 ]
 
+[[package]]
+name = "rustls-pemfile"
+version = "2.1.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "29993a25686778eb88d4189742cd713c9bce943bc54251a33509dc63cbacf73d"
+dependencies = [
+ "base64 0.22.1",
+ "rustls-pki-types",
+]
+
 [[package]]
 name = "rustls-pki-types"
-version = "1.5.0"
+version = "1.7.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "beb461507cee2c2ff151784c52762cf4d9ff6a61f3e80968600ed24fa837fa54"
+checksum = "976295e77ce332211c0d24d92c0e83e50f5c5f046d11082cea19f3df13a3562d"
 
 [[package]]
 name = "rustls-webpki"
-version = "0.102.3"
+version = "0.102.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f3bce581c0dd41bce533ce695a1437fa16a7ab5ac3ccfa99fe1a620a7885eabf"
+checksum = "ff448f7e92e913c4b7d4c6d8e4540a1724b319b4152b8aef6d4cf8339712b33e"
 dependencies = [
+ "aws-lc-rs",
  "ring",
  "rustls-pki-types",
  "untrusted",
@@ -3065,15 +3592,15 @@ dependencies = [
 
 [[package]]
 name = "rustversion"
-version = "1.0.15"
+version = "1.0.17"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "80af6f9131f277a45a3fba6ce8e2258037bb0477a67e610d3c1fe046ab31de47"
+checksum = "955d28af4278de8121b7ebeb796b6a45735dc01436d898801014aced2773a3d6"
 
 [[package]]
 name = "ryu"
-version = "1.0.17"
+version = "1.0.18"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e86697c916019a8588c99b5fac3cead74ec0b4b819707a682fd4d23fa0ce1ba1"
+checksum = "f3cb5ba0dc43242ce17de99c180e96db90b235b8a9fdc9543c96d2209116bd9f"
 
 [[package]]
 name = "safetensors"
@@ -3111,11 +3638,11 @@ checksum = "94143f37725109f92c262ed2cf5e59bce7498c01bcc1502d7b9afe439a4e9f49"
 
 [[package]]
 name = "security-framework"
-version = "2.10.0"
+version = "2.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "770452e37cad93e0a50d5abc3990d2bc351c36d0328f86cefec2f2fb206eaef6"
+checksum = "c627723fd09706bacdb5cf41499e95098555af3c3c29d014dc3c458ef6be11c0"
 dependencies = [
- "bitflags 1.3.2",
+ "bitflags 2.6.0",
  "core-foundation",
  "core-foundation-sys",
  "libc",
@@ -3124,9 +3651,9 @@ dependencies = [
 
 [[package]]
 name = "security-framework-sys"
-version = "2.10.0"
+version = "2.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "41f3cc463c0ef97e11c3461a9d3787412d30e8e7eb907c79180c4a57bf7c04ef"
+checksum = "317936bbbd05227752583946b9e66d7ce3b489f84e11a94a510b4437fef407d7"
 dependencies = [
  "core-foundation-sys",
  "libc",
@@ -3140,29 +3667,29 @@ checksum = "a3f0bf26fd526d2a95683cd0f87bf103b8539e2ca1ef48ce002d67aad59aa0b4"
 
 [[package]]
 name = "serde"
-version = "1.0.198"
+version = "1.0.203"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9846a40c979031340571da2545a4e5b7c4163bdae79b301d5f86d03979451fcc"
+checksum = "7253ab4de971e72fb7be983802300c30b5a7f0c2e56fab8abfc6a214307c0094"
 dependencies = [
  "serde_derive",
 ]
 
 [[package]]
 name = "serde_derive"
-version = "1.0.198"
+version = "1.0.203"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e88edab869b01783ba905e7d0153f9fc1a6505a96e4ad3018011eedb838566d9"
+checksum = "500cbc0ebeb6f46627f50f3f5811ccf6bf00643be300b4c3eabc0ef55dc5b5ba"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
 name = "serde_json"
-version = "1.0.116"
+version = "1.0.120"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3e17db7126d17feb94eb3fad46bf1a96b034e8aacbc2e775fe81505f8b0b2813"
+checksum = "4e0d21c9a8cae1235ad58a00c11cb40d4b1e5c784f1ef2c537876ed6ffd8b7c5"
 dependencies = [
  "itoa",
  "ryu",
@@ -3190,9 +3717,9 @@ dependencies = [
 
 [[package]]
 name = "serde_spanned"
-version = "0.6.5"
+version = "0.6.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "eb3622f419d1296904700073ea6cc23ad690adbd66f13ea683df73298736f0c1"
+checksum = "79e674e01f999af37c49f70a6ede167a8a60b2503e56c5599532a65baa5969a0"
 dependencies = [
  "serde",
 ]
@@ -3244,7 +3771,7 @@ checksum = "91d129178576168c589c9ec973feedf7d3126c01ac2bf08795109aa35b69fb8f"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -3267,6 +3794,12 @@ dependencies = [
  "lazy_static",
 ]
 
+[[package]]
+name = "shlex"
+version = "1.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0fda2ff0d084019ba4d7c6f371c95d8fd75ce3524c3cb8fb653a3023f6323e64"
+
 [[package]]
 name = "signal-hook-registry"
 version = "1.4.2"
@@ -3282,6 +3815,15 @@ version = "2.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "fa42c91313f1d05da9b26f267f931cf178d4aba455b4c4622dd7355eb80c6640"
 
+[[package]]
+name = "simsimd"
+version = "4.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "efc843bc8f12d9c8e6b734a0fe8918fc497b42f6ae0f347dbfdad5b5138ab9b4"
+dependencies = [
+ "cc",
+]
+
 [[package]]
 name = "sketches-ddsketch"
 version = "0.2.2"
@@ -3305,9 +3847,9 @@ checksum = "3c5e1a9a646d36c3599cd173a41282daf47c44583ad367b8e6837255952e5c67"
 
 [[package]]
 name = "socket2"
-version = "0.5.6"
+version = "0.5.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "05ffd9c0a93b7543e062e759284fcf5f5e3b098501104bfbdde4d404db792871"
+checksum = "ce305eb0b4296696835b71df73eb912e0f1ffd2556a501fcede6e0c50349191c"
 dependencies = [
  "libc",
  "windows-sys 0.52.0",
@@ -3350,21 +3892,34 @@ checksum = "a8f112729512f8e442d81f95a8a7ddf2b7c6b8a1a6f509a95864142b30cab2d3"
 
 [[package]]
 name = "strsim"
-version = "0.10.0"
+version = "0.11.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "73473c0e59e6d5812c5dfe2a064a6444949f089e20eec9a2e5506596494e4623"
+checksum = "7da8b5736845d9f2fcb837ea5d9e2628564b3b043a70948a3f0b778838c5fb4f"
 
 [[package]]
-name = "strsim"
-version = "0.11.1"
+name = "strum"
+version = "0.26.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7da8b5736845d9f2fcb837ea5d9e2628564b3b043a70948a3f0b778838c5fb4f"
+checksum = "8fec0f0aef304996cf250b31b5a10dee7980c85da9d759361292b8bca5a18f06"
+
+[[package]]
+name = "strum_macros"
+version = "0.26.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4c6bee85a5a24955dc440386795aa378cd9cf82acd5f764469152d2270e581be"
+dependencies = [
+ "heck 0.5.0",
+ "proc-macro2",
+ "quote",
+ "rustversion",
+ "syn 2.0.68",
+]
 
 [[package]]
 name = "subtle"
-version = "2.5.0"
+version = "2.6.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "81cdd64d312baedb58e21336b31bc043b77e01cc99033ce76ef539f78e965ebc"
+checksum = "13c2bddecc57b384dee18652358fb23172facb8a2c51ccc10d74c157bdea3292"
 
 [[package]]
 name = "syn"
@@ -3379,9 +3934,9 @@ dependencies = [
 
 [[package]]
 name = "syn"
-version = "2.0.60"
+version = "2.0.68"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "909518bc7b1c9b779f1bbf07f2929d35af9f0f37e47c6e9ef7f9dddc1e1821f3"
+checksum = "901fa70d88b9d6c98022e23b4136f9f3e54e4662c3bc1bd1d84a42a9a0f0c1e9"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -3408,7 +3963,7 @@ checksum = "c8af7666ab7b6390ab78131fb5b0fce11d6b7a6951602017c35fa82800708971"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -3417,7 +3972,7 @@ version = "0.5.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "ec7dddc5f0fee506baf8b9fdb989e242f17e4b11c61dfbb0635b705217199eea"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "byteorder",
  "enum-as-inner",
  "libc",
@@ -3448,9 +4003,9 @@ dependencies = [
 
 [[package]]
 name = "tar"
-version = "0.4.40"
+version = "0.4.41"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b16afcea1f22891c49a00c751c7b63b2233284064f11a200fc624137c51e2ddb"
+checksum = "cb797dad5fb5b76fcf519e702f4a589483b5ef06567f160c392832c1f5e44909"
 dependencies = [
  "filetime",
  "libc",
@@ -3471,11 +4026,14 @@ dependencies = [
 
 [[package]]
 name = "text-embeddings-backend"
-version = "1.2.3"
+version = "1.5.1"
 dependencies = [
  "clap",
+ "hf-hub",
+ "serde_json",
  "text-embeddings-backend-candle",
  "text-embeddings-backend-core",
+ "text-embeddings-backend-ort",
  "text-embeddings-backend-python",
  "tokio",
  "tracing",
@@ -3483,7 +4041,7 @@ dependencies = [
 
 [[package]]
 name = "text-embeddings-backend-candle"
-version = "1.2.3"
+version = "1.5.1"
 dependencies = [
  "accelerate-src",
  "anyhow",
@@ -3513,16 +4071,32 @@ dependencies = [
 
 [[package]]
 name = "text-embeddings-backend-core"
-version = "1.2.3"
+version = "1.5.1"
 dependencies = [
  "clap",
  "nohash-hasher",
  "thiserror",
 ]
 
+[[package]]
+name = "text-embeddings-backend-ort"
+version = "1.5.1"
+dependencies = [
+ "anyhow",
+ "ndarray",
+ "nohash-hasher",
+ "num_cpus",
+ "ort",
+ "serde",
+ "serde_json",
+ "text-embeddings-backend-core",
+ "thiserror",
+ "tracing",
+]
+
 [[package]]
 name = "text-embeddings-backend-python"
-version = "1.2.3"
+version = "1.5.1"
 dependencies = [
  "backend-grpc-client",
  "nohash-hasher",
@@ -3536,10 +4110,12 @@ dependencies = [
 
 [[package]]
 name = "text-embeddings-core"
-version = "1.2.3"
+version = "1.5.1"
 dependencies = [
+ "async-channel",
  "hf-hub",
  "metrics",
+ "serde_json",
  "text-embeddings-backend",
  "thiserror",
  "tokenizers",
@@ -3549,13 +4125,13 @@ dependencies = [
 
 [[package]]
 name = "text-embeddings-router"
-version = "1.2.3"
+version = "1.5.1"
 dependencies = [
  "anyhow",
  "async-stream",
  "axum 0.7.5",
  "axum-tracing-opentelemetry",
- "base64 0.21.7",
+ "base64 0.22.1",
  "clap",
  "futures",
  "hf-hub",
@@ -3563,16 +4139,19 @@ dependencies = [
  "init-tracing-opentelemetry",
  "insta",
  "is_close",
+ "libc",
  "metrics",
  "metrics-exporter-prometheus",
  "mimalloc",
  "num_cpus",
- "opentelemetry 0.20.0",
- "opentelemetry-otlp",
- "prost 0.12.4",
- "reqwest",
+ "opentelemetry 0.23.0",
+ "opentelemetry-otlp 0.16.0",
+ "opentelemetry_sdk 0.23.0",
+ "prost 0.12.6",
+ "reqwest 0.12.5",
  "serde",
  "serde_json",
+ "simsimd",
  "text-embeddings-backend",
  "text-embeddings-core",
  "thiserror",
@@ -3580,12 +4159,12 @@ dependencies = [
  "tokio",
  "tokio-stream",
  "tonic 0.11.0",
- "tonic-build 0.10.2",
+ "tonic-build 0.11.0",
  "tonic-health",
  "tonic-reflection",
  "tower-http",
  "tracing",
- "tracing-opentelemetry 0.21.0",
+ "tracing-opentelemetry 0.24.0",
  "tracing-subscriber",
  "utoipa",
  "utoipa-swagger-ui",
@@ -3595,22 +4174,22 @@ dependencies = [
 
 [[package]]
 name = "thiserror"
-version = "1.0.59"
+version = "1.0.61"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f0126ad08bff79f29fc3ae6a55cc72352056dfff61e3ff8bb7129476d44b23aa"
+checksum = "c546c80d6be4bc6a00c0f01730c08df82eaa7a7a61f11d656526506112cc1709"
 dependencies = [
  "thiserror-impl",
 ]
 
 [[package]]
 name = "thiserror-impl"
-version = "1.0.59"
+version = "1.0.61"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d1cd413b5d558b4c5bf3680e324a6fa5014e7b7c067a51e69dbdf47eb7148b66"
+checksum = "46c3384250002a6d5af4d114f2845d37b57521033f30d5c3f46c4d70e1197533"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -3658,9 +4237,9 @@ dependencies = [
 
 [[package]]
 name = "tinyvec"
-version = "1.6.0"
+version = "1.6.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "87cc5ceb3875bb20c2890005a4e226a4651264a5c75edb2421b52861a0a0cb50"
+checksum = "c55115c6fbe2d2bef26eb09ad74bde02d8255476fc0c7b515ef09fbb35742d82"
 dependencies = [
  "tinyvec_macros",
 ]
@@ -3692,7 +4271,7 @@ dependencies = [
  "rayon",
  "rayon-cond",
  "regex",
- "regex-syntax 0.8.3",
+ "regex-syntax 0.8.4",
  "serde",
  "serde_json",
  "spm_precompiled",
@@ -3704,9 +4283,9 @@ dependencies = [
 
 [[package]]
 name = "tokio"
-version = "1.37.0"
+version = "1.38.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1adbebffeca75fcfd058afa480fb6c0b81e165a0323f9c9d39c9697e37c46787"
+checksum = "ba4f4a02a7a80d6f274636f0aa95c7e383b912d41fe721a31f29e29698585a4a"
 dependencies = [
  "backtrace",
  "bytes",
@@ -3733,13 +4312,13 @@ dependencies = [
 
 [[package]]
 name = "tokio-macros"
-version = "2.2.0"
+version = "2.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5b8a1e28f2deaa14e508979454cb3a223b10b938b45af148bc0986de36f1923b"
+checksum = "5f5ae998a069d4b5aba8ee9dad856af7d520c3699e6159b185c2acd48155d39a"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -3752,6 +4331,17 @@ dependencies = [
  "tokio",
 ]
 
+[[package]]
+name = "tokio-rustls"
+version = "0.26.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0c7bc40d0e5a97695bb96e27995cd3a08538541b0a846f65bba7a359f36700d4"
+dependencies = [
+ "rustls 0.23.10",
+ "rustls-pki-types",
+ "tokio",
+]
+
 [[package]]
 name = "tokio-stream"
 version = "0.1.15"
@@ -3765,50 +4355,60 @@ dependencies = [
 
 [[package]]
 name = "tokio-util"
-version = "0.7.10"
+version = "0.7.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5419f34732d9eb6ee4c3578b7989078579b7f039cbbb9ca2c4da015749371e15"
+checksum = "9cf6b47b3771c49ac75ad09a6162f53ad4b8088b76ac60e8ec1455b31a189fe1"
 dependencies = [
  "bytes",
  "futures-core",
  "futures-sink",
  "pin-project-lite",
  "tokio",
- "tracing",
 ]
 
 [[package]]
 name = "toml"
-version = "0.8.12"
+version = "0.8.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e9dd1545e8208b4a5af1aa9bbd0b4cf7e9ea08fabc5d0a5c67fcaafa17433aa3"
+checksum = "6f49eb2ab21d2f26bd6db7bf383edc527a7ebaee412d17af4d40fdccd442f335"
 dependencies = [
  "serde",
  "serde_spanned",
  "toml_datetime",
- "toml_edit",
+ "toml_edit 0.22.14",
 ]
 
 [[package]]
 name = "toml_datetime"
-version = "0.6.5"
+version = "0.6.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3550f4e9685620ac18a50ed434eb3aec30db8ba93b0287467bca5826ea25baf1"
+checksum = "4badfd56924ae69bcc9039335b2e017639ce3f9b001c393c1b2d1ef846ce2cbf"
 dependencies = [
  "serde",
 ]
 
 [[package]]
 name = "toml_edit"
-version = "0.22.12"
+version = "0.21.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6a8534fd7f78b5405e860340ad6575217ce99f38d4d5c8f2442cb5ecb50090e1"
+dependencies = [
+ "indexmap 2.2.6",
+ "toml_datetime",
+ "winnow 0.5.40",
+]
+
+[[package]]
+name = "toml_edit"
+version = "0.22.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d3328d4f68a705b2a4498da1d580585d39a6510f98318a2cec3018a7ec61ddef"
+checksum = "f21c7aaf97f1bd9ca9d4f9e73b0a6c74bd5afef56f2bc931943a6e1c37e04e38"
 dependencies = [
  "indexmap 2.2.6",
  "serde",
  "serde_spanned",
  "toml_datetime",
- "winnow",
+ "winnow 0.6.13",
 ]
 
 [[package]]
@@ -3823,10 +4423,10 @@ dependencies = [
  "bytes",
  "futures-core",
  "futures-util",
- "h2",
+ "h2 0.3.26",
  "http 0.2.12",
  "http-body 0.4.6",
- "hyper 0.14.28",
+ "hyper 0.14.29",
  "hyper-timeout",
  "percent-encoding",
  "pin-project",
@@ -3850,14 +4450,14 @@ dependencies = [
  "axum 0.6.20",
  "base64 0.21.7",
  "bytes",
- "h2",
+ "h2 0.3.26",
  "http 0.2.12",
  "http-body 0.4.6",
- "hyper 0.14.28",
+ "hyper 0.14.29",
  "hyper-timeout",
  "percent-encoding",
  "pin-project",
- "prost 0.12.4",
+ "prost 0.12.6",
  "tokio",
  "tokio-stream",
  "tower",
@@ -3881,15 +4481,15 @@ dependencies = [
 
 [[package]]
 name = "tonic-build"
-version = "0.10.2"
+version = "0.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9d021fc044c18582b9a2408cd0dd05b1596e3ecdb5c4df822bb0183545683889"
+checksum = "be4ef6dd70a610078cb4e338a0f79d06bc759ff1b22d2120c2ff02ae264ba9c2"
 dependencies = [
- "prettyplease 0.2.19",
+ "prettyplease 0.2.20",
  "proc-macro2",
- "prost-build 0.12.4",
+ "prost-build 0.12.6",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -3899,7 +4499,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "2cef6e24bc96871001a7e48e820ab240b3de2201e59b517cf52835df2f1d2350"
 dependencies = [
  "async-stream",
- "prost 0.12.4",
+ "prost 0.12.6",
  "tokio",
  "tokio-stream",
  "tonic 0.11.0",
@@ -3911,8 +4511,8 @@ version = "0.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "548c227bd5c0fae5925812c4ec6c66ffcfced23ea370cb823f4d18f0fc1cb6a7"
 dependencies = [
- "prost 0.12.4",
- "prost-types 0.12.4",
+ "prost 0.12.6",
+ "prost-types 0.12.6",
  "tokio",
  "tokio-stream",
  "tonic 0.11.0",
@@ -3944,7 +4544,7 @@ version = "0.5.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1e9cd434a998747dd2c4276bc96ee2e0c7a2eadf3cae88e52be55a05fa9053f5"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "bytes",
  "http 1.1.0",
  "http-body 1.0.0",
@@ -3986,7 +4586,7 @@ checksum = "34704c8d6ebcbc939824180af020566b01a7c01f80641264eba0999f6c2b6be7"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -4037,30 +4637,32 @@ dependencies = [
 
 [[package]]
 name = "tracing-opentelemetry"
-version = "0.21.0"
+version = "0.23.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "75327c6b667828ddc28f5e3f169036cb793c3f588d83bf0f262a7f062ffed3c8"
+checksum = "a9be14ba1bbe4ab79e9229f7f89fab8d120b865859f10527f31c033e599d2284"
 dependencies = [
+ "js-sys",
  "once_cell",
- "opentelemetry 0.20.0",
- "opentelemetry_sdk 0.20.0",
+ "opentelemetry 0.22.0",
+ "opentelemetry_sdk 0.22.1",
  "smallvec",
  "tracing",
  "tracing-core",
- "tracing-log 0.1.4",
+ "tracing-log 0.2.0",
  "tracing-subscriber",
+ "web-time",
 ]
 
 [[package]]
 name = "tracing-opentelemetry"
-version = "0.22.0"
+version = "0.24.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c67ac25c5407e7b961fafc6f7e9aa5958fd297aada2d20fa2ae1737357e55596"
+checksum = "f68803492bf28ab40aeccaecc7021096bd256baf7ca77c3d425d89b35a7be4e4"
 dependencies = [
  "js-sys",
  "once_cell",
- "opentelemetry 0.21.0",
- "opentelemetry_sdk 0.21.2",
+ "opentelemetry 0.23.0",
+ "opentelemetry_sdk 0.23.0",
  "smallvec",
  "tracing",
  "tracing-core",
@@ -4071,14 +4673,14 @@ dependencies = [
 
 [[package]]
 name = "tracing-opentelemetry-instrumentation-sdk"
-version = "0.17.1"
+version = "0.18.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9db99a4f5224920c499515a737e2749eb9a19b729b3880afc24594524e9861de"
+checksum = "9a276058193f1b03d8279356215ec4c8c1bb21e40e5554bb239aa94fb2d8e189"
 dependencies = [
  "http 1.1.0",
- "opentelemetry 0.21.0",
+ "opentelemetry 0.22.0",
  "tracing",
- "tracing-opentelemetry 0.22.0",
+ "tracing-opentelemetry 0.23.0",
 ]
 
 [[package]]
@@ -4171,9 +4773,9 @@ checksum = "d4c87d22b6e3f4a18d4d40ef354e97c90fcb14dd91d7dc0aa9d8a1172ebf7202"
 
 [[package]]
 name = "unicode-width"
-version = "0.1.11"
+version = "0.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e51733f11c9c4f72aa0c160008246859e340b00807569a0da0e7a1079b27ba85"
+checksum = "0336d538f7abc86d282a4189614dfaa90810dfc2c6f6427eaf88e16311dd225d"
 
 [[package]]
 name = "unicode_categories"
@@ -4195,16 +4797,16 @@ checksum = "8ecb6da28b8a351d773b68d5825ac39017e680750f980f3a1a85cd8dd28a47c1"
 
 [[package]]
 name = "ureq"
-version = "2.9.6"
+version = "2.9.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "11f214ce18d8b2cbe84ed3aa6486ed3f5b285cf8d8fbdbce9f3f767a724adc35"
+checksum = "d11a831e3c0b56e438a28308e7c810799e3c118417f342d30ecec080105395cd"
 dependencies = [
- "base64 0.21.7",
+ "base64 0.22.1",
  "flate2",
  "log",
  "native-tls",
  "once_cell",
- "rustls",
+ "rustls 0.22.4",
  "rustls-pki-types",
  "rustls-webpki",
  "serde",
@@ -4216,9 +4818,9 @@ dependencies = [
 
 [[package]]
 name = "url"
-version = "2.5.0"
+version = "2.5.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "31e6302e3bb753d46e83516cae55ae196fc0c309407cf11ab35cc51a4c2a4633"
+checksum = "22784dbdf76fdde8af1aeda5622b546b422b6fc585325248a2bf9f5e41e94d6c"
 dependencies = [
  "form_urlencoded",
  "idna",
@@ -4233,15 +4835,15 @@ checksum = "daf8dba3b7eb870caf1ddeed7bc9d2a049f3cfdfae7cb521b087cc33ae4c49da"
 
 [[package]]
 name = "utf8parse"
-version = "0.2.1"
+version = "0.2.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "711b9620af191e0cdc7468a8d14e709c3dcdb115b36f838e601583af800a370a"
+checksum = "06abde3611657adf66d383f00b093d7faecc7fa57071cce2578660c9f1010821"
 
 [[package]]
 name = "utoipa"
-version = "4.2.0"
+version = "4.2.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "272ebdfbc99111033031d2f10e018836056e4d2c8e2acda76450ec7974269fa7"
+checksum = "c5afb1a60e207dca502682537fefcfd9921e71d0b83e9576060f09abc6efab23"
 dependencies = [
  "indexmap 2.2.6",
  "serde",
@@ -4251,38 +4853,40 @@ dependencies = [
 
 [[package]]
 name = "utoipa-gen"
-version = "4.2.0"
+version = "4.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d3c9f4d08338c1bfa70dde39412a040a884c6f318b3d09aaaf3437a1e52027fc"
+checksum = "7bf0e16c02bc4bf5322ab65f10ab1149bdbcaa782cba66dc7057370a3f8190be"
 dependencies = [
  "proc-macro-error",
  "proc-macro2",
  "quote",
  "regex",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
 name = "utoipa-swagger-ui"
-version = "6.0.0"
+version = "7.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0b39868d43c011961e04b41623e050aedf2cc93652562ff7935ce0f819aaf2da"
+checksum = "943e0ff606c6d57d410fd5663a4d7c074ab2c5f14ab903b9514565e59fa1189e"
 dependencies = [
  "axum 0.7.5",
  "mime_guess",
  "regex",
+ "reqwest 0.12.5",
  "rust-embed",
  "serde",
  "serde_json",
+ "url",
  "utoipa",
- "zip",
+ "zip 1.1.4",
 ]
 
 [[package]]
 name = "uuid"
-version = "1.8.0"
+version = "1.9.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a183cf7feeba97b4dd1c0d46788634f6221d87fa961b305bed08c851829efcc0"
+checksum = "5de17fd2f7da591098415cff336e12965a28061ddace43b59cb3c430179c9439"
 dependencies = [
  "getrandom",
 ]
@@ -4317,7 +4921,7 @@ checksum = "cff2381c6b31ab2555441e382d699a56c3551d0cfdf0c4df5617bf271c1dd102"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -4384,7 +4988,7 @@ dependencies = [
  "once_cell",
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
  "wasm-bindgen-shared",
 ]
 
@@ -4418,7 +5022,7 @@ checksum = "e94f17b526d0a461a191c78ea52bbce64071ed5c04c9ffe424dcb38f74171bb7"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
  "wasm-bindgen-backend",
  "wasm-bindgen-shared",
 ]
@@ -4441,9 +5045,9 @@ dependencies = [
 
 [[package]]
 name = "web-time"
-version = "0.2.4"
+version = "1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "aa30049b1c872b72c89866d458eae9f20380ab280ffd1b1e18df2d3e2d98cfe0"
+checksum = "5a6580f308b1fad9207618087a65c04e7a10bc77e02c8e84e9b00dd4b12fa0bb"
 dependencies = [
  "js-sys",
  "wasm-bindgen",
@@ -4451,9 +5055,9 @@ dependencies = [
 
 [[package]]
 name = "webpki-roots"
-version = "0.26.1"
+version = "0.26.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b3de34ae270483955a94f4b21bdaaeb83d508bb84a01435f393818edb0012009"
+checksum = "bd7c23921eeb1713a4e851530e9b9756e4fb0e89978582942612524cf09f01cd"
 dependencies = [
  "rustls-pki-types",
 ]
@@ -4651,9 +5255,18 @@ checksum = "bec47e5bfd1bff0eeaf6d8b485cc1074891a197ab4225d504cb7a1ab88b02bf0"
 
 [[package]]
 name = "winnow"
-version = "0.6.6"
+version = "0.5.40"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f593a95398737aeed53e489c785df13f3618e41dbcd6718c6addbf1395aa6876"
+dependencies = [
+ "memchr",
+]
+
+[[package]]
+name = "winnow"
+version = "0.6.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f0c976aaaa0e1f90dbb21e9587cdaf1d9679a1cde8875c0d6bd83ab96a208352"
+checksum = "59b5e5f6c299a3c7890b876a2a587f3115162487e704907d9b6cd29473052ba1"
 dependencies = [
  "memchr",
 ]
@@ -4668,6 +5281,16 @@ dependencies = [
  "windows-sys 0.48.0",
 ]
 
+[[package]]
+name = "winreg"
+version = "0.52.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a277a57398d4bfa075df44f501a17cfdf8542d224f0d36095a2adc7aee4ef0a5"
+dependencies = [
+ "cfg-if",
+ "windows-sys 0.48.0",
+]
+
 [[package]]
 name = "xattr"
 version = "1.3.1"
@@ -4690,9 +5313,9 @@ dependencies = [
 
 [[package]]
 name = "yoke"
-version = "0.7.3"
+version = "0.7.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "65e71b2e4f287f467794c671e2b8f8a5f3716b3c829079a1c44740148eff07e4"
+checksum = "6c5b1314b079b0930c31e3af543d8ee1757b1951ae1e1565ec704403a7240ca5"
 dependencies = [
  "serde",
  "stable_deref_trait",
@@ -4702,62 +5325,76 @@ dependencies = [
 
 [[package]]
 name = "yoke-derive"
-version = "0.7.3"
+version = "0.7.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9e6936f0cce458098a201c245a11bef556c6a0181129c7034d10d76d1ec3a2b8"
+checksum = "28cc31741b18cb6f1d5ff12f5b7523e3d6eb0852bbbad19d73905511d9849b95"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
  "synstructure",
 ]
 
 [[package]]
 name = "zerocopy"
-version = "0.7.32"
+version = "0.7.34"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "74d4d3961e53fa4c9a25a8637fc2bfaf2595b3d3ae34875568a5cf64787716be"
+checksum = "ae87e3fcd617500e5d106f0380cf7b77f3c6092aae37191433159dda23cfb087"
 dependencies = [
  "zerocopy-derive",
 ]
 
 [[package]]
 name = "zerocopy-derive"
-version = "0.7.32"
+version = "0.7.34"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9ce1b18ccd8e73a9321186f97e46f9f04b778851177567b1975109d26a08d2a6"
+checksum = "15e934569e47891f7d9411f1a451d947a60e000ab3bd24fbb970f000387d1b3b"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
 ]
 
 [[package]]
 name = "zerofrom"
-version = "0.1.3"
+version = "0.1.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "655b0814c5c0b19ade497851070c640773304939a6c0fd5f5fb43da0696d05b7"
+checksum = "91ec111ce797d0e0784a1116d0ddcdbea84322cd79e5d5ad173daeba4f93ab55"
 dependencies = [
  "zerofrom-derive",
 ]
 
 [[package]]
 name = "zerofrom-derive"
-version = "0.1.3"
+version = "0.1.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e6a647510471d372f2e6c2e6b7219e44d8c574d24fdc11c610a61455782f18c3"
+checksum = "0ea7b4a3637ea8669cedf0f1fd5c286a17f3de97b8dd5a70a6c167a1730e63a5"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.60",
+ "syn 2.0.68",
  "synstructure",
 ]
 
 [[package]]
 name = "zeroize"
-version = "1.8.0"
+version = "1.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ced3678a2879b30306d323f4542626697a464a97c0a07c9aebf7ebca65cd4dde"
+dependencies = [
+ "zeroize_derive",
+]
+
+[[package]]
+name = "zeroize_derive"
+version = "1.4.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "63381fa6624bf92130a6b87c0d07380116f80b565c42cf0d754136f0238359ef"
+checksum = "ce36e65b0d2999d2aafac989fb249189a141aee1f53c612c1f37d72631959f69"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.68",
+]
 
 [[package]]
 name = "zip"
@@ -4768,5 +5405,20 @@ dependencies = [
  "byteorder",
  "crc32fast",
  "crossbeam-utils",
+]
+
+[[package]]
+name = "zip"
+version = "1.1.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9cc23c04387f4da0374be4533ad1208cbb091d5c11d070dfef13676ad6497164"
+dependencies = [
+ "arbitrary",
+ "crc32fast",
+ "crossbeam-utils",
+ "displaydoc",
  "flate2",
+ "indexmap 2.2.6",
+ "num_enum",
+ "thiserror",
 ]
diff --git a/Cargo.toml b/Cargo.toml
index f8198679..3d13f7de 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -2,6 +2,7 @@
 members = [
     "backends",
     "backends/candle",
+    "backends/ort",
     "backends/core",
     "backends/python",
     "backends/grpc-client",
@@ -11,18 +12,32 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "1.2.3"
+version = "1.5.1"
 edition = "2021"
 authors = ["Olivier Dehaene"]
 homepage = "https://github.com/huggingface/text-embeddings-inference"
 
+[workspace.dependencies]
+anyhow = "1.0.75"
+clap = { version = "4.1", features = ["derive", "env"] }
+hf-hub = { version = "0.3.2", features = ["tokio", "online"], default-features = false }
+metrics = "0.23"
+nohash-hasher = "0.2"
+num_cpus = "1.16.0"
+tokenizers = { version = "0.19.1", default-features = false, features = ["onig", "esaxx_fast"] }
+tokio = { version = "1.25", features = ["rt", "rt-multi-thread", "parking_lot", "sync", "signal"] }
+tracing = "0.1"
+serde = { version = "1.0", features = ["serde_derive"] }
+serde_json = "1.0"
+thiserror = "1.0"
+
+
 [patch.crates-io]
 cudarc = { git = "https://github.com/coreylowman/cudarc", rev = "c388e724af93a3e8fbe484f5ded2d8b3c1badd8e" }
-candle = { git = "https://github.com/OlivierDehaene/candle", rev = "33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3", package = "candle-core" }
-candle-nn = { git = "https://github.com/OlivierDehaene/candle", rev = "33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3", package = "candle-nn" }
-candle-transformers = { git = "https://github.com/OlivierDehaene/candle", rev = "33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3", package = "candle-transformers" }
-candle-flash-attn = { git = "https://github.com/OlivierDehaene/candle", rev = "33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3", package = "candle-flash-attn" }
-hf-hub = { git = "https://github.com/huggingface/hf-hub", rev = "b167f69692be5f49eb8003788f7f8a499a98b096" }
+candle = { git = "https://github.com/OlivierDehaene/candle", rev = "7e02ad856104799b73a946ac1e153f0de77feaaf", package = "candle-core" }
+candle-nn = { git = "https://github.com/OlivierDehaene/candle", rev = "7e02ad856104799b73a946ac1e153f0de77feaaf", package = "candle-nn" }
+candle-transformers = { git = "https://github.com/OlivierDehaene/candle", rev = "7e02ad856104799b73a946ac1e153f0de77feaaf", package = "candle-transformers" }
+candle-flash-attn = { git = "https://github.com/OlivierDehaene/candle", rev = "7e02ad856104799b73a946ac1e153f0de77feaaf", package = "candle-flash-attn" }
 
 [profile.release]
 debug = 0
diff --git a/Dockerfile b/Dockerfile
index 8c1368c0..152f2738 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -28,22 +28,9 @@ ARG ACTIONS_CACHE_URL
 ARG ACTIONS_RUNTIME_TOKEN
 ARG SCCACHE_GHA_ENABLED
 
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
-| gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
-  echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | \
-  tee /etc/apt/sources.list.d/oneAPI.list
-
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-    intel-oneapi-mkl-devel=2024.0.0-49656 \
-    build-essential \
-    && rm -rf /var/lib/apt/lists/*
-
-RUN echo "int mkl_serv_intel_cpu_true() {return 1;}" > fakeintel.c && \
-    gcc -shared -fPIC -o libfakeintel.so fakeintel.c
-
 COPY --from=planner /usr/src/recipe.json recipe.json
 
-RUN cargo chef cook --release --features candle --features mkl-dynamic --no-default-features --recipe-path recipe.json && sccache -s
+RUN cargo chef cook --release --features ort --no-default-features --recipe-path recipe.json && sccache -s
 
 COPY backends backends
 COPY core core
@@ -51,11 +38,11 @@ COPY router router
 COPY Cargo.toml ./
 COPY Cargo.lock ./
 
-FROM builder as http-builder
+FROM builder AS http-builder
 
-RUN cargo build --release --bin text-embeddings-router -F candle -F mkl-dynamic -F http --no-default-features && sccache -s
+RUN cargo build --release --bin text-embeddings-router -F ort -F http --no-default-features && sccache -s
 
-FROM builder as grpc-builder
+FROM builder AS grpc-builder
 
 RUN PROTOC_ZIP=protoc-21.12-linux-x86_64.zip && \
     curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP && \
@@ -65,37 +52,21 @@ RUN PROTOC_ZIP=protoc-21.12-linux-x86_64.zip && \
 
 COPY proto proto
 
-RUN cargo build --release --bin text-embeddings-router -F grpc -F candle -F mkl-dynamic --no-default-features && sccache -s
+RUN cargo build --release --bin text-embeddings-router -F grpc -F ort --no-default-features && sccache -s
 
-FROM debian:bookworm-slim as base
+FROM debian:bookworm-slim AS base
 
 ENV HUGGINGFACE_HUB_CACHE=/data \
-    PORT=80 \
-    MKL_ENABLE_INSTRUCTIONS=AVX512_E4 \
-    RAYON_NUM_THREADS=8 \
-    LD_PRELOAD=/usr/local/libfakeintel.so \
-    LD_LIBRARY_PATH=/usr/local/lib
+    PORT=80
 
 RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-    libomp-dev \
     ca-certificates \
     libssl-dev \
     curl \
     && rm -rf /var/lib/apt/lists/*
 
-# Copy a lot of the Intel shared objects because of the mkl_serv_intel_cpu_true patch...
-COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_intel_lp64.so.2 /usr/local/lib/libmkl_intel_lp64.so.2
-COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_intel_thread.so.2 /usr/local/lib/libmkl_intel_thread.so.2
-COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_core.so.2 /usr/local/lib/libmkl_core.so.2
-COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_vml_def.so.2 /usr/local/lib/libmkl_vml_def.so.2
-COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_def.so.2 /usr/local/lib/libmkl_def.so.2
-COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_vml_avx2.so.2 /usr/local/lib/libmkl_vml_avx2.so.2
-COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_vml_avx512.so.2 /usr/local/lib/libmkl_vml_avx512.so.2
-COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_avx2.so.2 /usr/local/lib/libmkl_avx2.so.2
-COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_avx512.so.2 /usr/local/lib/libmkl_avx512.so.2
-COPY --from=builder /usr/src/libfakeintel.so /usr/local/libfakeintel.so
 
-FROM base as grpc
+FROM base AS grpc
 
 COPY --from=grpc-builder /usr/src/target/release/text-embeddings-router /usr/local/bin/text-embeddings-router
 
@@ -107,7 +78,7 @@ FROM base AS http
 COPY --from=http-builder /usr/src/target/release/text-embeddings-router /usr/local/bin/text-embeddings-router
 
 # Amazon SageMaker compatible image
-FROM http as sagemaker
+FROM http AS sagemaker
 COPY --chmod=775 sagemaker-entrypoint.sh entrypoint.sh
 
 ENTRYPOINT ["./entrypoint.sh"]
diff --git a/Dockerfile-arm64 b/Dockerfile-arm64
deleted file mode 100644
index e170a534..00000000
--- a/Dockerfile-arm64
+++ /dev/null
@@ -1,92 +0,0 @@
-FROM lukemathwalker/cargo-chef:latest-rust-1.75-bookworm AS chef
-
-WORKDIR /usr/src
-
-ENV SCCACHE=0.5.4
-ENV RUSTC_WRAPPER=/usr/local/bin/sccache
-
-# Donwload and configure sccache
-RUN curl -fsSL https://github.com/mozilla/sccache/releases/download/v$SCCACHE/sccache-v$SCCACHE-x86_64-unknown-linux-musl.tar.gz | tar -xzv --strip-components=1 -C /usr/local/bin sccache-v$SCCACHE-x86_64-unknown-linux-musl/sccache && \
-    chmod +x /usr/local/bin/sccache
-
-FROM chef AS planner
-
-COPY backends backends
-COPY core core
-COPY router router
-COPY Cargo.toml ./
-COPY Cargo.lock ./
-
-RUN cargo chef prepare  --recipe-path recipe.json
-
-FROM chef AS builder
-
-ARG GIT_SHA
-ARG DOCKER_LABEL
-
-# sccache specific variables
-ARG ACTIONS_CACHE_URL
-ARG ACTIONS_RUNTIME_TOKEN
-ARG SCCACHE_GHA_ENABLED
-
-RUN echo "int mkl_serv_intel_cpu_true() {return 1;}" > fakeintel.c && \
-    gcc -shared -fPIC -o libfakeintel.so fakeintel.c
-
-COPY --from=planner /usr/src/recipe.json recipe.json
-
-RUN cargo chef cook --release --features candle --no-default-features --recipe-path recipe.json && sccache -s
-
-COPY backends backends
-COPY core core
-COPY router router
-COPY Cargo.toml ./
-COPY Cargo.lock ./
-
-FROM builder as http-builder
-
-RUN cargo build --release --bin text-embeddings-router -F candle -F http --no-default-features && sccache -s
-
-FROM builder as grpc-builder
-
-RUN PROTOC_ZIP=protoc-21.12-linux-x86_64.zip && \
-    curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP && \
-    unzip -o $PROTOC_ZIP -d /usr/local bin/protoc && \
-    unzip -o $PROTOC_ZIP -d /usr/local 'include/*' && \
-    rm -f $PROTOC_ZIP
-
-COPY proto proto
-
-RUN cargo build --release --bin text-embeddings-router -F grpc -F candle --no-default-features && sccache -s
-
-FROM debian:bookworm-slim as base
-
-COPY --from=builder /usr/src/libfakeintel.so /usr/local/libfakeintel.so
-
-ENV HUGGINGFACE_HUB_CACHE=/data \
-    PORT=80 \
-    MKL_ENABLE_INSTRUCTIONS=AVX512_E4 \
-    RAYON_NUM_THREADS=8 \
-    LD_PRELOAD=/usr/local/libfakeintel.so \
-    LD_LIBRARY_PATH=/usr/local/lib
-
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-    libomp-dev \
-    ca-certificates \
-    libssl-dev \
-    curl \
-    && rm -rf /var/lib/apt/lists/*
-
-
-FROM base as grpc
-
-COPY --from=grpc-builder /usr/src/target/release/text-embeddings-router /usr/local/bin/text-embeddings-router
-
-ENTRYPOINT ["text-embeddings-router"]
-CMD ["--json-output"]
-
-FROM base
-
-COPY --from=http-builder /usr/src/target/release/text-embeddings-router /usr/local/bin/text-embeddings-router
-
-ENTRYPOINT ["text-embeddings-router"]
-CMD ["--json-output"]
diff --git a/Dockerfile-cuda b/Dockerfile-cuda
index d67eba93..1e39fb1e 100644
--- a/Dockerfile-cuda
+++ b/Dockerfile-cuda
@@ -75,7 +75,7 @@ COPY router router
 COPY Cargo.toml ./
 COPY Cargo.lock ./
 
-FROM builder as http-builder
+FROM builder AS http-builder
 
 RUN if [ ${CUDA_COMPUTE_CAP} -ge 75 -a ${CUDA_COMPUTE_CAP} -lt 80 ]; \
     then \
@@ -84,7 +84,7 @@ RUN if [ ${CUDA_COMPUTE_CAP} -ge 75 -a ${CUDA_COMPUTE_CAP} -lt 80 ]; \
         cargo build --release --bin text-embeddings-router -F candle-cuda -F static-linking -F http --no-default-features && sccache -s; \
     fi;
 
-FROM builder as grpc-builder
+FROM builder AS grpc-builder
 
 RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
     unzip \
@@ -105,7 +105,7 @@ RUN if [ ${CUDA_COMPUTE_CAP} -ge 75 -a ${CUDA_COMPUTE_CAP} -lt 80 ]; \
         cargo build --release --bin text-embeddings-router -F candle-cuda -F static-linking -F grpc --no-default-features && sccache -s; \
     fi;
 
-FROM nvidia/cuda:12.2.0-base-ubuntu22.04 as base
+FROM nvidia/cuda:12.2.0-base-ubuntu22.04 AS base
 
 ARG DEFAULT_USE_FLASH_ATTENTION=True
 
@@ -113,7 +113,13 @@ ENV HUGGINGFACE_HUB_CACHE=/data \
     PORT=80 \
     USE_FLASH_ATTENTION=$DEFAULT_USE_FLASH_ATTENTION
 
-FROM base as grpc
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+    ca-certificates \
+    libssl-dev \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+FROM base AS grpc
 
 COPY --from=grpc-builder /usr/src/target/release/text-embeddings-router /usr/local/bin/text-embeddings-router
 
diff --git a/Dockerfile-cuda-all b/Dockerfile-cuda-all
index 71321705..851a3c97 100644
--- a/Dockerfile-cuda-all
+++ b/Dockerfile-cuda-all
@@ -110,7 +110,7 @@ RUN if [ $VERTEX = "true" ]; \
 
 RUN mv /usr/src/target/release/text-embeddings-router /usr/src/target/release/text-embeddings-router-90
 
-FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04 as base
+FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04 AS base
 
 ARG DEFAULT_USE_FLASH_ATTENTION=True
 
@@ -118,6 +118,12 @@ ENV HUGGINGFACE_HUB_CACHE=/data \
     PORT=80 \
     USE_FLASH_ATTENTION=$DEFAULT_USE_FLASH_ATTENTION
 
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+    ca-certificates \
+    libssl-dev \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
 COPY --from=builder /usr/src/target/release/text-embeddings-router-75 /usr/local/bin/text-embeddings-router-75
 COPY --from=builder /usr/src/target/release/text-embeddings-router-80 /usr/local/bin/text-embeddings-router-80
 COPY --from=builder /usr/src/target/release/text-embeddings-router-90 /usr/local/bin/text-embeddings-router-90
diff --git a/README.md b/README.md
index e9306fda..002bcdfc 100644
--- a/README.md
+++ b/README.md
@@ -33,6 +33,7 @@ length of 512 tokens:
     - [Docker Images](#docker-images)
     - [API Documentation](#api-documentation)
     - [Using a private or gated model](#using-a-private-or-gated-model)
+    - [Air gapped deployment](#air-gapped-deployment)
     - [Using Re-rankers models](#using-re-rankers-models)
     - [Using Sequence Classification models](#using-sequence-classification-models)
     - [Using SPLADE pooling](#using-splade-pooling)
@@ -63,30 +64,33 @@ Ember, GTE and E5. TEI implements many features such as:
 
 #### Text Embeddings
 
-You can use any JinaBERT model with Alibi or absolute positions or any BERT, CamemBERT, RoBERTa, or XLM-RoBERTa model
-with absolute positions in `text-embeddings-inference`.
+Text Embeddings Inference currently supports Nomic, BERT, CamemBERT, XLM-RoBERTa models with absolute positions, JinaBERT
+model with Alibi positions and Mistral, Alibaba GTE and Qwen2 models with Rope positions.
 
-**Support for other model types will be added in the future.**
+Below are some examples of the currently supported models:
 
-Examples of supported models:
+| MTEB Rank | Model Size          | Model Type  | Model ID                                                                                         |
+|-----------|---------------------|-------------|--------------------------------------------------------------------------------------------------|
+| 1         | 7B (Very Expensive) | Mistral     | [Salesforce/SFR-Embedding-2_R](https://hf.co/Salesforce/SFR-Embedding-2_R)                       |
+| 2         | 7B (Very Expensive) | Qwen2       | [Alibaba-NLP/gte-Qwen2-7B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-7B-instruct)             |
+| 9         | 1.5B (Expensive)    | Qwen2       | [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct)         |
+| 15        | 0.4B                | Alibaba GTE | [Alibaba-NLP/gte-large-en-v1.5](https://hf.co/Alibaba-NLP/gte-large-en-v1.5)                     |
+| 20        | 0.3B                | Bert        | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1)                                   |
+| 24        | 0.5B                | XLM-RoBERTa | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
+| N/A       | 0.1B                | NomicBert   | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1)                       |
+| N/A       | 0.1B                | NomicBert   | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5)                   |
+| N/A       | 0.1B                | JinaBERT    | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en)             |
+| N/A       | 0.1B                | JinaBERT    | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jinaai/jina-embeddings-v2-base-code)         |
 
-| MTEB Rank | Model Type  | Model ID                                                                                         |
-|-----------|-------------|--------------------------------------------------------------------------------------------------|
-| 6         | Bert        | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1)                                   |
-| 10        | XLM-RoBERTa | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
-| N/A       | NomicBert   | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1)                       |
-| N/A       | NomicBert   | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5)                   |
-| N/A       | JinaBERT    | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en)             |
-| N/A       | JinaBERT    | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jinaai/jina-embeddings-v2-base-code)         |
 
-You can explore the list of best performing text embeddings
-models [here](https://huggingface.co/spaces/mteb/leaderboard).
+To explore the list of best performing text embeddings models, visit the
+[Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
 
 #### Sequence Classification and Re-Ranking
 
-`text-embeddings-inference` v0.4.0 added support for Bert, CamemBERT, RoBERTa and XLM-RoBERTa Sequence Classification models.
+Text Embeddings Inference currently supports CamemBERT, and XLM-RoBERTa Sequence Classification models with absolute positions.
 
-Example of supported sequence classification models:
+Below are some examples of the currently supported models:
 
 | Task               | Model Type  | Model ID                                                                                    |
 |--------------------|-------------|---------------------------------------------------------------------------------------------|
@@ -98,10 +102,9 @@ Example of supported sequence classification models:
 
 ```shell
 model=BAAI/bge-large-en-v1.5
-revision=refs/pr/5
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model --revision $revision
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5 --model-id $model
 ```
 
 And then you can make requests like
@@ -163,9 +166,11 @@ Options:
           [env: POOLING=]
 
           Possible values:
-          - cls:    Select the CLS token as embedding
-          - mean:   Apply Mean pooling to the model embeddings
-          - splade: Apply SPLADE (Sparse Lexical and Expansion) to the model embeddings. This option is only available if the loaded model is a `ForMaskedLM` Transformer model
+          - cls:        Select the CLS token as embedding
+          - mean:       Apply Mean pooling to the model embeddings
+          - splade:     Apply SPLADE (Sparse Lexical and Expansion) to the model embeddings. This option is only
+          available if the loaded model is a `ForMaskedLM` Transformer model
+          - last-token: Select the last token as embedding
 
       --max-concurrent-requests <MAX_CONCURRENT_REQUESTS>
           The maximum amount of concurrent requests for this particular deployment.
@@ -199,6 +204,37 @@ Options:
           [env: MAX_CLIENT_BATCH_SIZE=]
           [default: 32]
 
+      --auto-truncate
+          Automatically truncate inputs that are longer than the maximum supported size
+
+          Unused for gRPC servers
+
+          [env: AUTO_TRUNCATE=]
+
+      --default-prompt-name <DEFAULT_PROMPT_NAME>
+          The name of the prompt that should be used by default for encoding. If not set, no prompt will be applied.
+
+          Must be a key in the `sentence-transformers` configuration `prompts` dictionary.
+
+          For example if ``default_prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...}, then the
+          sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" because
+          the prompt text will be prepended before any text to encode.
+
+          The argument '--default-prompt-name <DEFAULT_PROMPT_NAME>' cannot be used with '--default-prompt <DEFAULT_PROMPT>`
+
+          [env: DEFAULT_PROMPT_NAME=]
+
+      --default-prompt <DEFAULT_PROMPT>
+          The prompt that should be used by default for encoding. If not set, no prompt will be applied.
+
+          For example if ``default_prompt`` is "query: " then the sentence "What is the capital of France?" will be
+          encoded as "query: What is the capital of France?" because the prompt text will be prepended before any text
+          to encode.
+
+          The argument '--default-prompt <DEFAULT_PROMPT>' cannot be used with '--default-prompt-name <DEFAULT_PROMPT_NAME>`
+
+          [env: DEFAULT_PROMPT=]
+
       --hf-api-token <HF_API_TOKEN>
           Your HuggingFace hub token
 
@@ -224,9 +260,10 @@ Options:
           [default: /tmp/text-embeddings-inference-server]
 
       --huggingface-hub-cache <HUGGINGFACE_HUB_CACHE>
-          The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance
+          The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk
+          for instance
 
-          [env: HUGGINGFACE_HUB_CACHE=/data]
+          [env: HUGGINGFACE_HUB_CACHE=]
 
       --payload-limit <PAYLOAD_LIMIT>
           Payload size limit in bytes
@@ -239,7 +276,8 @@ Options:
       --api-key <API_KEY>
           Set an api key for request authorization.
 
-          By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token.
+          By default the server responds to every request. With an api key set, the requests must have the Authorization
+          header set with the api key as Bearer token.
 
           [env: API_KEY=]
 
@@ -254,12 +292,14 @@ Options:
           [env: OTLP_ENDPOINT=]
 
       --otlp-service-name <OTLP_SERVICE_NAME>
-          The service name for opentelemetry.
+          The service name for opentelemetry. e.g. `text-embeddings-inference.server`
 
           [env: OTLP_SERVICE_NAME=]
           [default: text-embeddings-inference.server]
 
       --cors-allow-origin <CORS_ALLOW_ORIGIN>
+          Unused for gRPC servers
+
           [env: CORS_ALLOW_ORIGIN=]
 ```
 
@@ -269,13 +309,13 @@ Text Embeddings Inference ships with multiple Docker images that you can use to
 
 | Architecture                        | Image                                                                   |
 |-------------------------------------|-------------------------------------------------------------------------|
-| CPU                                 | ghcr.io/huggingface/text-embeddings-inference:cpu-1.2                   |
+| CPU                                 | ghcr.io/huggingface/text-embeddings-inference:cpu-1.5                   |
 | Volta                               | NOT SUPPORTED                                                           |
-| Turing (T4, RTX 2000 series, ...)   | ghcr.io/huggingface/text-embeddings-inference:turing-1.2 (experimental) |
-| Ampere 80 (A100, A30)               | ghcr.io/huggingface/text-embeddings-inference:1.2                       |
-| Ampere 86 (A10, A40, ...)           | ghcr.io/huggingface/text-embeddings-inference:86-1.2                    |
-| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-1.2                    |
-| Hopper (H100)                       | ghcr.io/huggingface/text-embeddings-inference:hopper-1.2 (experimental) |
+| Turing (T4, RTX 2000 series, ...)   | ghcr.io/huggingface/text-embeddings-inference:turing-1.5 (experimental) |
+| Ampere 80 (A100, A30)               | ghcr.io/huggingface/text-embeddings-inference:1.5                       |
+| Ampere 86 (A10, A40, ...)           | ghcr.io/huggingface/text-embeddings-inference:86-1.5                    |
+| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-1.5                    |
+| Hopper (H100)                       | ghcr.io/huggingface/text-embeddings-inference:hopper-1.5 (experimental) |
 
 **Warning**: Flash Attention is turned off by default for the Turing image as it suffers from precision issues.
 You can turn Flash Attention v1 ON by using the `USE_FLASH_ATTENTION=True` environment variable.
@@ -288,14 +328,14 @@ at: [https://huggingface.github.io/text-embeddings-inference](https://huggingfac
 
 ### Using a private or gated model
 
-You have the option to utilize the `HUGGING_FACE_HUB_TOKEN` environment variable for configuring the token employed by
+You have the option to utilize the `HF_API_TOKEN` environment variable for configuring the token employed by
 `text-embeddings-inference`. This allows you to gain access to protected resources.
 
 For example:
 
 1. Go to https://huggingface.co/settings/tokens
 2. Copy your cli READ token
-3. Export `HUGGING_FACE_HUB_TOKEN=<your cli READ token>`
+3. Export `HF_API_TOKEN=<your cli READ token>`
 
 or with Docker:
 
@@ -304,7 +344,30 @@ model=<your private model>
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 token=<your cli READ token>
 
-docker run --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model
+docker run --gpus all -e HF_API_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5 --model-id $model
+```
+
+### Air gapped deployment
+
+To deploy Text Embeddings Inference in an air-gapped environment, first download the weights and then mount them inside
+the container using a volume.
+
+For example:
+
+```shell
+# (Optional) create a `models` directory
+mkdir models
+cd models
+
+# Make sure you have git-lfs installed (https://git-lfs.com)
+git lfs install
+git clone https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5
+
+# Set the models directory as the volume path
+volume=$PWD
+
+# Mount the models directory inside the container with a volume and set the model ID
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5 --model-id /data/gte-base-en-v1.5
 ```
 
 ### Using Re-rankers models
@@ -319,10 +382,9 @@ downstream performance.
 
 ```shell
 model=BAAI/bge-reranker-large
-revision=refs/pr/4
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model --revision $revision
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5 --model-id $model
 ```
 
 And then you can rank the similarity between a query and a list of texts with:
@@ -342,7 +404,7 @@ You can also use classic Sequence Classification models like `SamLowe/roberta-ba
 model=SamLowe/roberta-base-go_emotions
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5 --model-id $model
 ```
 
 Once you have deployed the model you can use the `predict` endpoint to get the emotions most associated with an input:
@@ -362,7 +424,7 @@ You can choose to activate SPLADE pooling for Bert and Distilbert MaskedLM archi
 model=naver/efficient-splade-VI-BT-large-query
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model --pooling splade
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5 --model-id $model --pooling splade
 ```
 
 Once you have deployed the model you can use the `/embed_sparse` endpoint to get the sparse embedding:
@@ -382,16 +444,16 @@ by setting the address to an OTLP collector with the `--otlp-endpoint` argument.
 ### gRPC
 
 `text-embeddings-inference` offers a gRPC API as an alternative to the default HTTP API for high performance
-deployments. The API protobuf definition can be found [here](https://github.com/huggingface/text-embeddings-inference/blob/main/proto/tei.proto).
+deployments. The API protobuf definition can be
+found [here](https://github.com/huggingface/text-embeddings-inference/blob/main/proto/tei.proto).
 
 You can use the gRPC API by adding the `-grpc` tag to any TEI Docker image. For example:
 
 ```shell
 model=BAAI/bge-large-en-v1.5
-revision=refs/pr/5
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2-grpc --model-id $model --revision $revision
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5-grpc --model-id $model
 ```
 
 ```shell
@@ -423,9 +485,8 @@ You can now launch Text Embeddings Inference on CPU with:
 
 ```shell
 model=BAAI/bge-large-en-v1.5
-revision=refs/pr/5
 
-text-embeddings-router --model-id $model --revision $revision --port 8080
+text-embeddings-router --model-id $model --port 8080
 ```
 
 **Note:** on some machines, you may also need the OpenSSL libraries and gcc. On Linux machines, run:
@@ -438,7 +499,8 @@ sudo apt-get install libssl-dev gcc -y
 
 GPUs with Cuda compute capabilities < 7.5 are not supported (V100, Titan V, GTX 1000 series, ...).
 
-Make sure you have Cuda and the nvidia drivers installed. NVIDIA drivers on your device need to be compatible with CUDA version 12.2 or higher.
+Make sure you have Cuda and the nvidia drivers installed. NVIDIA drivers on your device need to be compatible with CUDA
+version 12.2 or higher.
 You also need to add the nvidia binaries to your path:
 
 ```shell
@@ -461,9 +523,8 @@ You can now launch Text Embeddings Inference on GPU with:
 
 ```shell
 model=BAAI/bge-large-en-v1.5
-revision=refs/pr/5
 
-text-embeddings-router --model-id $model --revision $revision --port 8080
+text-embeddings-router --model-id $model --port 8080
 ```
 
 ## Docker build
@@ -499,12 +560,18 @@ docker build . -f Dockerfile-cuda --build-arg CUDA_COMPUTE_CAP=$runtime_compute_
 ```
 
 ### Apple M1/M2 arm64 architectures
+
 #### DISCLAIMER
-As explained here [MPS-Ready, ARM64 Docker Image](https://github.com/pytorch/pytorch/issues/81224), Metal / MPS is not supported via Docker. As such inference will be CPU bound and most likely pretty slow when using this docker image on an M1/M2 ARM CPU.
+
+As explained here [MPS-Ready, ARM64 Docker Image](https://github.com/pytorch/pytorch/issues/81224), Metal / MPS is not
+supported via Docker. As such inference will be CPU bound and most likely pretty slow when using this docker image on an
+M1/M2 ARM CPU.
+
 ```
-docker build . -f Dockerfile-arm64 --platform=linux/arm64
+docker build . -f Dockerfile --platform=linux/arm64
 ```
 
 ## Examples
+
 - [Set up an Inference Endpoint with TEI](https://huggingface.co/learn/cookbook/automatic_embedding_tei_inference_endpoints)
 - [RAG containers with TEI](https://github.com/plaggy/rag-containers)
diff --git a/backends/Cargo.toml b/backends/Cargo.toml
index 3a6797cb..f29283ad 100644
--- a/backends/Cargo.toml
+++ b/backends/Cargo.toml
@@ -6,16 +6,20 @@ authors.workspace = true
 homepage.workspace = true
 
 [dependencies]
-clap = { version = "4.1.4", features = ["derive"], optional = true }
+clap = { workspace = true, optional = true }
+hf-hub = { workspace = true }
+serde_json = { workspace = true }
 text-embeddings-backend-core = { path = "core" }
 text-embeddings-backend-python = { path = "python", optional = true }
 text-embeddings-backend-candle = { path = "candle", optional = true }
-tokio = { version = "^1.25", features = ["sync"] }
-tracing = "^0.1"
+text-embeddings-backend-ort = { path = "ort", optional = true }
+tokio = { workspace = true }
+tracing = { workspace = true }
 
 [features]
 clap = ["dep:clap", "text-embeddings-backend-core/clap"]
 python = ["dep:text-embeddings-backend-python"]
+ort = ["dep:text-embeddings-backend-ort"]
 candle = ["dep:text-embeddings-backend-candle"]
 cuda = ["text-embeddings-backend-candle?/cuda"]
 metal = ["text-embeddings-backend-candle?/metal"]
diff --git a/backends/candle/Cargo.toml b/backends/candle/Cargo.toml
index 9fb300b9..f26d213a 100644
--- a/backends/candle/Cargo.toml
+++ b/backends/candle/Cargo.toml
@@ -6,7 +6,7 @@ authors.workspace = true
 homepage.workspace = true
 
 [dependencies]
-anyhow = "^1.0"
+anyhow = { workspace = true }
 accelerate-src = { version = "0.3.2", optional = true }
 intel-mkl-src = { version = "0.8.1", optional = true  }
 candle = { version = "*", package = "candle-core", default-features = false }
@@ -17,21 +17,21 @@ candle-flash-attn-v1 = { git = "https://github.com/huggingface/candle-flash-attn
 candle-cublaslt = { git = "https://github.com/huggingface/candle-cublaslt", rev = "cf789b7dd6d4abb19b03b9556442f94f0588b4a0", optional = true }
 candle-layer-norm = { git = "https://github.com/huggingface/candle-layer-norm", rev = "94c2add7d94c2d63aebde77f7534614e04dbaea1", optional = true }
 candle-rotary = { git = "https://github.com/huggingface/candle-rotary", rev = "0a718a0856569a92f3112e64f10d07e4447822e8", optional = true }
-nohash-hasher = "^0.2"
+nohash-hasher = { workspace = true }
 text-embeddings-backend-core = { path = "../core" }
-tracing = "^0.1"
+tracing = { workspace = true }
 safetensors = "^0.4"
-thiserror = "^1.0"
-serde = { version = "^1.0", features = ["serde_derive"] }
-serde_json = "^1.0"
+thiserror = { workspace = true }
+serde = { workspace = true }
+serde_json = { workspace = true }
 memmap2 = "^0.9"
 
 [dev-dependencies]
 insta = { git = "https://github.com/OlivierDehaene/insta", rev = "f4f98c0410b91fb5a28b10df98e4422955be9c2c", features = ["yaml"] }
 is_close = "0.1.3"
 hf-hub = "0.3.2"
-anyhow = "1.0.75"
-tokenizers = { version = "^0.19.1", default-features = false, features = ["onig", "esaxx_fast"] }
+anyhow = { workspace = true }
+tokenizers = { workspace = true }
 serial_test = "2.0.0"
 
 [build-dependencies]
diff --git a/backends/candle/src/flash_attn.rs b/backends/candle/src/flash_attn.rs
index 3afc6517..f2016928 100644
--- a/backends/candle/src/flash_attn.rs
+++ b/backends/candle/src/flash_attn.rs
@@ -31,6 +31,7 @@ pub(crate) fn flash_attn_varlen(
     max_seqlen_k: usize,
     softmax_scale: f32,
     causal: bool,
+    window_size_left: Option<usize>,
 ) -> Result<Tensor, candle::Error> {
     let runtime_compute_cap = get_runtime_compute_cap();
 
@@ -38,6 +39,9 @@ pub(crate) fn flash_attn_varlen(
         if alibi_slopes.is_some() {
             candle::bail!("Flash attention v1 does not support alibi");
         }
+        if window_size_left.is_some() {
+            candle::bail!("Flash attention v1 does not support attention windowing");
+        }
 
         #[cfg(feature = "flash-attn-v1")]
         {
@@ -59,10 +63,12 @@ pub(crate) fn flash_attn_varlen(
     } else if (80..90).contains(&runtime_compute_cap) || runtime_compute_cap == 90 {
         #[cfg(feature = "flash-attn")]
         {
-            use candle_flash_attn::{flash_attn_varlen, flash_attn_varlen_alibi};
+            use candle_flash_attn::{flash_attn_varlen_alibi_windowed, flash_attn_varlen_windowed};
+
+            let window_size_right = if causal { Some(0) } else { None };
 
             let attention = if let Some(alibi_slopes) = alibi_slopes {
-                flash_attn_varlen_alibi(
+                flash_attn_varlen_alibi_windowed(
                     q,
                     k,
                     v,
@@ -72,10 +78,11 @@ pub(crate) fn flash_attn_varlen(
                     max_seqlen_q,
                     max_seqlen_k,
                     softmax_scale,
-                    causal,
+                    window_size_left,
+                    window_size_right,
                 )
             } else {
-                flash_attn_varlen(
+                flash_attn_varlen_windowed(
                     q,
                     k,
                     v,
@@ -84,7 +91,8 @@ pub(crate) fn flash_attn_varlen(
                     max_seqlen_q,
                     max_seqlen_k,
                     softmax_scale,
-                    causal,
+                    window_size_left,
+                    window_size_right,
                 )
             };
 
diff --git a/backends/candle/src/layers/linear.rs b/backends/candle/src/layers/linear.rs
index 3fdd025b..fc8af1dd 100644
--- a/backends/candle/src/layers/linear.rs
+++ b/backends/candle/src/layers/linear.rs
@@ -7,6 +7,7 @@ use serde::Deserialize;
 pub enum HiddenAct {
     Gelu,
     Relu,
+    #[serde(alias = "silu")]
     Swiglu,
 }
 
diff --git a/backends/candle/src/layers/mod.rs b/backends/candle/src/layers/mod.rs
index 8e108fc2..81f63310 100644
--- a/backends/candle/src/layers/mod.rs
+++ b/backends/candle/src/layers/mod.rs
@@ -2,7 +2,11 @@
 mod cublaslt;
 mod layer_norm;
 mod linear;
+#[allow(dead_code, unused)]
+mod rms_norm;
 
 pub use cublaslt::get_cublas_lt_wrapper;
 pub use layer_norm::LayerNorm;
 pub use linear::{HiddenAct, Linear};
+#[allow(unused_imports)]
+pub use rms_norm::RMSNorm;
diff --git a/backends/candle/src/layers/rms_norm.rs b/backends/candle/src/layers/rms_norm.rs
new file mode 100644
index 00000000..e7dab642
--- /dev/null
+++ b/backends/candle/src/layers/rms_norm.rs
@@ -0,0 +1,96 @@
+use candle::{DType, Device, Result, Tensor, D};
+use candle_nn::VarBuilder;
+
+#[derive(Debug)]
+pub struct RMSNorm {
+    weight: Tensor,
+    epsilon: f32,
+    span: tracing::Span,
+}
+
+impl RMSNorm {
+    pub fn load(vb: VarBuilder, hidden_size: usize, epsilon: f32) -> Result<Self> {
+        Ok(Self {
+            weight: vb
+                .get(hidden_size, "weight")
+                .or_else(|_| vb.get(hidden_size, "gamma"))?,
+            epsilon,
+            span: tracing::span!(tracing::Level::TRACE, "rms-norm"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        residual: Option<&Tensor>,
+    ) -> Result<(Tensor, Tensor)> {
+        let _enter = self.span.enter();
+
+        match hidden_states.device() {
+            Device::Cpu | Device::Metal(_) => {
+                let mut hidden_states = hidden_states.clone();
+                let residual_add = if let Some(residual) = residual {
+                    let residual_add = hidden_states.add(residual)?;
+                    hidden_states = residual_add.clone();
+                    residual_add
+                } else {
+                    hidden_states.clone()
+                };
+
+                let hidden_states_dtype = hidden_states.dtype();
+                let internal_dtype = match hidden_states_dtype {
+                    DType::F16 | DType::BF16 => DType::F32,
+                    d => d,
+                };
+                let hidden_size = hidden_states.dim(D::Minus1)?;
+                let hidden_states = hidden_states.to_dtype(internal_dtype)?;
+                let norm_hidden_states =
+                    (hidden_states.sqr()?.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
+                let hidden_states_normed = hidden_states
+                    .broadcast_div(&(norm_hidden_states + self.epsilon as f64)?.sqrt()?)?;
+                Ok((
+                    hidden_states_normed
+                        .to_dtype(hidden_states_dtype)?
+                        .broadcast_mul(&self.weight)?,
+                    residual_add,
+                ))
+            }
+            Device::Cuda(_) => {
+                #[cfg(feature = "cuda")]
+                {
+                    use candle_layer_norm::{fused_add_rms_norm, rms_norm};
+
+                    let original_shape = hidden_states.shape();
+                    let hidden_states = hidden_states.flatten_to(D::Minus2)?;
+
+                    if let Some(residual) = residual {
+                        let residual = residual.flatten_to(D::Minus2)?;
+
+                        let (result, residual_add) = fused_add_rms_norm(
+                            &hidden_states,
+                            &residual,
+                            &self.weight,
+                            None,
+                            self.epsilon,
+                        )?;
+                        Ok((
+                            result.reshape(original_shape)?,
+                            residual_add.reshape(original_shape)?,
+                        ))
+                    } else {
+                        let residual_add = hidden_states.clone();
+
+                        let result = rms_norm(&hidden_states, &self.weight, None, self.epsilon)?;
+
+                        Ok((
+                            result.reshape(original_shape)?,
+                            residual_add.reshape(original_shape)?,
+                        ))
+                    }
+                }
+                #[cfg(not(feature = "cuda"))]
+                candle::bail!("`cuda` feature is not enabled")
+            }
+        }
+    }
+}
diff --git a/backends/candle/src/lib.rs b/backends/candle/src/lib.rs
index a7a5dcf0..acd93750 100644
--- a/backends/candle/src/lib.rs
+++ b/backends/candle/src/lib.rs
@@ -11,13 +11,13 @@ use crate::compute_cap::{
     compatible_compute_cap, get_compile_compute_cap, get_runtime_compute_cap,
 };
 use crate::models::{
-    BertConfig, BertModel, DistilBertConfig, DistilBertModel, JinaBertModel, JinaCodeBertModel,
-    Model, NomicBertModel, NomicConfig,
+    BertConfig, BertModel, DistilBertConfig, DistilBertModel, GTEConfig, JinaBertModel,
+    JinaCodeBertModel, MistralConfig, Model, NomicBertModel, NomicConfig, Qwen2Config,
 };
 #[cfg(feature = "cuda")]
 use crate::models::{
-    FlashBertModel, FlashDistilBertModel, FlashJinaBertModel, FlashJinaCodeBertModel,
-    FlashNomicBertModel,
+    FlashBertModel, FlashDistilBertModel, FlashGTEModel, FlashJinaBertModel,
+    FlashJinaCodeBertModel, FlashMistralModel, FlashNomicBertModel, FlashQwen2Model,
 };
 use anyhow::Context;
 use candle::{DType, Device};
@@ -56,6 +56,10 @@ enum Config {
     DistilBert(DistilBertConfig),
     #[serde(rename(deserialize = "nomic_bert"))]
     NomicBert(NomicConfig),
+    Mistral(MistralConfig),
+    #[serde(rename = "new")]
+    Gte(GTEConfig),
+    Qwen2(Qwen2Config),
 }
 
 pub struct CandleBackend {
@@ -69,6 +73,54 @@ impl CandleBackend {
         dtype: String,
         model_type: ModelType,
     ) -> Result<Self, BackendError> {
+        // Default files
+        let default_safetensors = model_path.join("model.safetensors");
+        let default_pytorch = model_path.join("pytorch_model.bin");
+
+        // Single Files
+        let model_files = if default_safetensors.exists() {
+            vec![default_safetensors]
+        } else if default_pytorch.exists() {
+            vec![default_pytorch]
+        }
+        // Sharded weights
+        else {
+            // Get index file
+            let index_file = model_path.join("model.safetensors.index.json");
+
+            // Parse file
+            let index_file_string: String = std::fs::read_to_string(&index_file)
+                .map_err(|err| BackendError::Start(err.to_string()))?;
+            let json: serde_json::Value = serde_json::from_str(&index_file_string)
+                .map_err(|err| BackendError::Start(err.to_string()))?;
+
+            let weight_map = match json.get("weight_map") {
+                None => {
+                    return Err(BackendError::Start(format!(
+                        "no weight map in {index_file:?}"
+                    )));
+                }
+                Some(serde_json::Value::Object(map)) => map,
+                Some(_) => {
+                    return Err(BackendError::Start(format!(
+                        "weight map in {index_file:?} is not a map"
+                    )));
+                }
+            };
+            let mut safetensors_files = std::collections::HashSet::new();
+            for value in weight_map.values() {
+                if let Some(file) = value.as_str() {
+                    safetensors_files.insert(file.to_string());
+                }
+            }
+
+            // Collect paths
+            safetensors_files
+                .iter()
+                .map(|n| model_path.join(n))
+                .collect()
+        };
+
         // Load config
         let config: String = std::fs::read_to_string(model_path.join("config.json"))
             .context("Unable to read config file")
@@ -115,17 +167,10 @@ impl CandleBackend {
             )))
         }?;
 
-        let safetensors_path = model_path.join("model.safetensors");
-        let vb = if safetensors_path.exists() {
-            unsafe {
-                VarBuilder::from_mmaped_safetensors(
-                    &[model_path.join("model.safetensors")],
-                    dtype,
-                    &device,
-                )
-            }
+        let vb = if model_files.len() == 1 && model_files[0].extension().unwrap() == "bin" {
+            VarBuilder::from_pth(&model_files[0], dtype, &device)
         } else {
-            VarBuilder::from_pth(model_path.join("pytorch_model.bin"), dtype, &device)
+            unsafe { VarBuilder::from_mmaped_safetensors(&model_files, dtype, &device) }
         }
         .s()?;
 
@@ -136,7 +181,7 @@ impl CandleBackend {
             )),
             (Config::Bert(config), Device::Cpu | Device::Metal(_)) => match config {
                 BertConfigWrapper::JinaBert(config) => {
-                    tracing::info!("Starting JinaBertModel model on {:?}", device);
+                    tracing::info!("Starting JinaBert model on {:?}", device);
                     Ok(Box::new(JinaBertModel::load(vb, &config, model_type).s()?))
                 }
                 BertConfigWrapper::JinaCodeBert(config) => {
@@ -160,15 +205,27 @@ impl CandleBackend {
                 ))
             }
             (Config::DistilBert(config), Device::Cpu | Device::Metal(_)) => {
-                tracing::info!("Starting DistilBertModel model on {:?}", device);
+                tracing::info!("Starting DistilBert model on {:?}", device);
                 Ok(Box::new(
                     DistilBertModel::load(vb, &config, model_type).s()?,
                 ))
             }
             (Config::NomicBert(config), Device::Cpu | Device::Metal(_)) => {
-                tracing::info!("Starting NomicBertModel model on {:?}", device);
+                tracing::info!("Starting NomicBert model on {:?}", device);
                 Ok(Box::new(NomicBertModel::load(vb, &config, model_type).s()?))
             }
+            (Config::Mistral(_), Device::Cpu | Device::Metal(_)) => Err(BackendError::Start(
+                "Mistral is only supported on Cuda devices in fp16 with flash attention enabled"
+                    .to_string(),
+            )),
+            (Config::Gte(_), Device::Cpu | Device::Metal(_)) => Err(BackendError::Start(
+                "GTE is only supported on Cuda devices in fp16 with flash attention enabled"
+                    .to_string(),
+            )),
+            (Config::Qwen2(_), Device::Cpu | Device::Metal(_)) => Err(BackendError::Start(
+                "Qwen2 is only supported on Cuda devices in fp16 with flash attention enabled"
+                    .to_string(),
+            )),
             #[cfg(feature = "cuda")]
             (Config::Bert(config), Device::Cuda(_)) => {
                 if cfg!(any(feature = "flash-attn", feature = "flash-attn-v1"))
@@ -198,7 +255,7 @@ impl CandleBackend {
                 } else {
                     match config {
                         BertConfigWrapper::JinaBert(config) => {
-                            tracing::info!("Starting JinaBertModel model on {:?}", device);
+                            tracing::info!("Starting JinaBert model on {:?}", device);
                             Ok(Box::new(JinaBertModel::load(vb, &config, model_type).s()?))
                         }
                         BertConfigWrapper::JinaCodeBert(config) => {
@@ -245,7 +302,7 @@ impl CandleBackend {
                         .to_lowercase()
                         == "true"
                 {
-                    tracing::info!("Starting FlashDistilBertModel model on {:?}", device);
+                    tracing::info!("Starting FlashDistilBert model on {:?}", device);
                     Ok(Box::new(
                         FlashDistilBertModel::load(vb, &config, model_type).s()?,
                     ))
@@ -265,15 +322,50 @@ impl CandleBackend {
                         .to_lowercase()
                         == "true"
                 {
-                    tracing::info!("Starting FlashNomicBertModel model on {:?}", device);
+                    tracing::info!("Starting FlashNomicBert model on {:?}", device);
                     Ok(Box::new(
                         FlashNomicBertModel::load(vb, &config, model_type).s()?,
                     ))
                 } else {
-                    tracing::info!("Starting NomicBertModel model on {:?}", device);
+                    tracing::info!("Starting NomicBert model on {:?}", device);
                     Ok(Box::new(NomicBertModel::load(vb, &config, model_type).s()?))
                 }
             }
+            #[cfg(feature = "cuda")]
+            (Config::Mistral(config), Device::Cuda(_)) => {
+                if dtype != DType::F16
+                    || !cfg!(feature = "flash-attn")
+                    || get_runtime_compute_cap().unwrap() < 80
+                {
+                    return Err(BackendError::Start("Mistral is only supported on Cuda devices in fp16 with flash attention v2 enabled".to_string()));
+                }
+                tracing::info!("Starting FlashMistral model on {:?}", device);
+                Ok(Box::new(
+                    FlashMistralModel::load(vb, &config, model_type).s()?,
+                ))
+            }
+            #[cfg(feature = "cuda")]
+            (Config::Gte(config), Device::Cuda(_)) => {
+                if dtype != DType::F16
+                    || !cfg!(any(feature = "flash-attn", feature = "flash-attn-v1"))
+                {
+                    return Err(BackendError::Start("GTE is only supported on Cuda devices in fp16 with flash attention enabled".to_string()));
+                }
+                tracing::info!("Starting FlashGTE model on {:?}", device);
+                Ok(Box::new(FlashGTEModel::load(vb, &config, model_type).s()?))
+            }
+            #[cfg(feature = "cuda")]
+            (Config::Qwen2(config), Device::Cuda(_)) => {
+                if dtype != DType::F16
+                    || !cfg!(any(feature = "flash-attn", feature = "flash-attn-v1"))
+                {
+                    return Err(BackendError::Start("Qwen2 is only supported on Cuda devices in fp16 with flash attention v2 enabled".to_string()));
+                }
+                tracing::info!("Starting FlashQwen2 model on {:?}", device);
+                Ok(Box::new(
+                    FlashQwen2Model::load(vb, &config, model_type).s()?,
+                ))
+            }
         };
 
         Ok(Self {
diff --git a/backends/candle/src/models/bert.rs b/backends/candle/src/models/bert.rs
index d12d90d2..32880d44 100644
--- a/backends/candle/src/models/bert.rs
+++ b/backends/candle/src/models/bert.rs
@@ -35,6 +35,7 @@ pub enum PositionEmbeddingType {
     #[default]
     Absolute,
     Alibi,
+    Rope,
 }
 
 #[derive(Debug)]
@@ -638,6 +639,10 @@ impl BertModel {
                 (pool, Some(classifier), None)
             }
             ModelType::Embedding(pool) => {
+                if pool == Pool::LastToken {
+                    candle::bail!("`last_token` is not supported for Bert");
+                }
+
                 let splade = if pool == Pool::Splade {
                     Some(BertSpladeHead::load_roberta(vb.clone(), config)?)
                 } else {
@@ -799,7 +804,7 @@ impl BertModel {
         let input_ids = Tensor::from_vec(input_ids, shape, &self.device)?;
         let type_ids = Tensor::from_vec(type_ids, shape, &self.device)?;
         let position_ids = Tensor::from_vec(position_ids, shape, &self.device)?;
-        let input_lengths =
+        let mut input_lengths =
             Tensor::from_vec(input_lengths, (batch_size, 1), &self.device)?.to_dtype(self.dtype)?;
 
         let embedding_output = self
@@ -832,6 +837,8 @@ impl BertModel {
             let pooled_embeddings = match self.pool {
                 // CLS pooling
                 Pool::Cls => outputs.i((.., 0))?,
+                // Last token pooling is not supported for this model
+                Pool::LastToken => unreachable!(),
                 // Mean pooling
                 Pool::Mean => {
                     if let Some(ref attention_mask) = attention_mask {
@@ -840,6 +847,7 @@ impl BertModel {
                         if let Some(pooled_indices) = pooled_indices {
                             // Select values in the batch
                             attention_mask = attention_mask.index_select(&pooled_indices, 0)?;
+                            input_lengths = input_lengths.index_select(&pooled_indices, 0)?;
                         };
 
                         // Mask padded values
diff --git a/backends/candle/src/models/distilbert.rs b/backends/candle/src/models/distilbert.rs
index e7145309..2cf62081 100644
--- a/backends/candle/src/models/distilbert.rs
+++ b/backends/candle/src/models/distilbert.rs
@@ -389,7 +389,12 @@ impl DistilBertModel {
             ModelType::Classifier => {
                 candle::bail!("`classifier` model type is not supported for DistilBert")
             }
-            ModelType::Embedding(pool) => pool,
+            ModelType::Embedding(pool) => {
+                if pool == Pool::LastToken {
+                    candle::bail!("`last_token` is not supported for DistilBert");
+                }
+                pool
+            }
         };
 
         let (embeddings, encoder) = match (
@@ -564,6 +569,8 @@ impl DistilBertModel {
             let pooled_embeddings = match self.pool {
                 // CLS pooling
                 Pool::Cls => outputs.i((.., 0))?,
+                // Last token pooling is not supported for this model
+                Pool::LastToken => unreachable!(),
                 // Mean pooling
                 Pool::Mean => {
                     if let Some(ref attention_mask) = attention_mask {
diff --git a/backends/candle/src/models/flash_bert.rs b/backends/candle/src/models/flash_bert.rs
index d248c91f..8951dcc1 100644
--- a/backends/candle/src/models/flash_bert.rs
+++ b/backends/candle/src/models/flash_bert.rs
@@ -5,7 +5,7 @@ use crate::models::bert::{
     PositionEmbeddingType, RobertaClassificationHead,
 };
 use crate::models::Model;
-use candle::{DType, Device, Result, Tensor};
+use candle::{DType, Device, IndexOp, Result, Tensor};
 use candle_nn::VarBuilder;
 use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
@@ -103,6 +103,7 @@ impl BertAttention {
             max_s,
             self.softmax_scale,
             false,
+            None,
         )?;
         let attention = attention.flatten_from(candle::D::Minus2)?;
 
@@ -393,27 +394,47 @@ impl FlashBertModel {
 
         let pooled_embeddings = if has_pooling_requests {
             match self.pool {
-                // CLS pooling
-                Pool::Cls => {
-                    // Get the indices of the cls tokens from cu_seqlens
-                    let mut cls_indices = cu_seqlens.narrow(0, 0, batch_size)?;
-
-                    // If raw_indices is empty, we don't need to do anything with
-                    // the pooled_indices
-                    if has_raw_requests {
-                        // We need the pooled indices to select the correct cls indices
-                        let pooled_indices = Tensor::from_vec(
-                            batch.pooled_indices.clone(),
-                            batch.pooled_indices.len(),
-                            &self.device,
-                        )?;
-
-                        // Only select indices that requires pooling
-                        cls_indices = cls_indices.index_select(&pooled_indices, 0)?
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
+                    if batch_size > 1 {
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
+
+                        // If raw_indices is empty, we don't need to do anything with
+                        // the pooled_indices
+                        if has_raw_requests {
+                            // We need the pooled indices to select the correct cls indices
+                            let pooled_indices = Tensor::from_vec(
+                                batch.pooled_indices.clone(),
+                                batch.pooled_indices.len(),
+                                &self.device,
+                            )?;
+
+                            // Only select indices that requires pooling
+                            indices = indices.index_select(&pooled_indices, 0)?
+                        }
+
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
+                    } else {
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
                     }
-
-                    // Select cls tokens
-                    Some(outputs.index_select(&cls_indices, 0)?)
                 }
                 // Mean pooling
                 Pool::Mean => {
diff --git a/backends/candle/src/models/flash_distilbert.rs b/backends/candle/src/models/flash_distilbert.rs
index 3d30c3a8..7f060601 100644
--- a/backends/candle/src/models/flash_distilbert.rs
+++ b/backends/candle/src/models/flash_distilbert.rs
@@ -4,7 +4,7 @@ use crate::models::distilbert::{
     DistilBertConfig, DistilBertEmbeddings, DistilBertMLP, DistilBertSpladeHead,
 };
 use crate::models::Model;
-use candle::{DType, Device, Result, Tensor};
+use candle::{DType, Device, IndexOp, Result, Tensor};
 use candle_nn::VarBuilder;
 use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
@@ -84,6 +84,7 @@ impl DistilBertAttention {
             max_s,
             self.softmax_scale,
             false,
+            None,
         )?;
         let attention = attention.flatten_from(candle::D::Minus2)?;
 
@@ -259,27 +260,45 @@ impl FlashDistilBertModel {
 
         let pooled_embeddings = if has_pooling_requests {
             let pooled_embeddings = match self.pool {
-                // CLS pooling
-                Pool::Cls => {
-                    // Get the indices of the cls tokens from cu_seqlens
-                    let mut cls_indices = cu_seqlens.narrow(0, 0, batch_size)?;
-
-                    // If raw_indices is empty, we don't need to do anything with
-                    // the pooled_indices
-                    if has_raw_requests {
-                        // We need the pooled indices to select the correct cls indices
-                        let pooled_indices = Tensor::from_vec(
-                            batch.pooled_indices.clone(),
-                            batch.pooled_indices.len(),
-                            &self.device,
-                        )?;
-
-                        // Only select indices that requires pooling
-                        cls_indices = cls_indices.index_select(&pooled_indices, 0)?
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
+                    if batch_size > 1 {
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
+
+                        // If raw_indices is empty, we don't need to do anything with
+                        // the pooled_indices
+                        if has_raw_requests {
+                            // We need the pooled indices to select the correct cls indices
+                            let pooled_indices = Tensor::from_vec(
+                                batch.pooled_indices.clone(),
+                                batch.pooled_indices.len(),
+                                &self.device,
+                            )?;
+
+                            // Only select indices that requires pooling
+                            indices = indices.index_select(&pooled_indices, 0)?
+                        }
+
+                        // Select tokens
+                        outputs.index_select(&indices, 0)?
+                    } else {
+                        match self.pool {
+                            Pool::Cls => outputs.i(0)?,
+                            Pool::LastToken => {
+                                outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                            }
+                            _ => unreachable!(),
+                        }
+                        .unsqueeze(0)?
                     }
-
-                    // Select cls tokens
-                    outputs.index_select(&cls_indices, 0)?
                 }
                 // Mean pooling
                 Pool::Mean => {
diff --git a/backends/candle/src/models/flash_gte.rs b/backends/candle/src/models/flash_gte.rs
new file mode 100644
index 00000000..f3aec220
--- /dev/null
+++ b/backends/candle/src/models/flash_gte.rs
@@ -0,0 +1,463 @@
+use crate::flash_attn::flash_attn_varlen;
+use crate::layers::{HiddenAct, LayerNorm, Linear};
+use crate::models::{GTEConfig, Model, NTKScaling, PositionEmbeddingType, RopeScaling};
+use candle::{DType, Device, IndexOp, Result, Tensor};
+use candle_nn::{Embedding, Module, VarBuilder};
+use text_embeddings_backend_core::{Batch, ModelType, Pool};
+
+struct GTEAttention {
+    qkv_linear: Linear,
+    o_proj: Linear,
+
+    num_attention_heads: usize,
+    attention_head_size: usize,
+
+    softmax_scale: f32,
+
+    span: tracing::Span,
+}
+
+impl GTEAttention {
+    pub fn load(vb: VarBuilder, config: &GTEConfig) -> Result<Self> {
+        let num_attention_heads = config.num_attention_heads;
+        let attention_head_size = config.hidden_size / config.num_attention_heads;
+        let hidden_size = config.hidden_size;
+
+        let qkv_weight = vb
+            .pp("qkv_proj")
+            .get((hidden_size * 3, hidden_size), "weight")?;
+        let qkv_bias = vb.pp("qkv_proj").get(hidden_size * 3, "bias")?;
+
+        let qkv_linear = Linear::new(qkv_weight, Some(qkv_bias), None);
+
+        let o_proj_weight = vb.pp("o_proj").get((hidden_size, hidden_size), "weight")?;
+        let o_proj_bias = vb.pp("o_proj").get(hidden_size, "bias")?;
+
+        let o_proj = Linear::new(o_proj_weight, Some(o_proj_bias), None);
+
+        let softmax_scale = (1. / (attention_head_size as f64).sqrt()) as f32;
+
+        Ok(Self {
+            qkv_linear,
+            o_proj,
+            num_attention_heads,
+            attention_head_size,
+            softmax_scale,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let qkv = self.qkv_linear.forward(hidden_states)?;
+
+        // Reshape to [tokens, heads, head_size]
+        let mut new_qkv_shape = qkv.dims().to_vec();
+        new_qkv_shape.pop();
+        new_qkv_shape.push(self.num_attention_heads * 3);
+        new_qkv_shape.push(self.attention_head_size);
+
+        let qkv = qkv.reshape(new_qkv_shape)?;
+
+        // Split qkv tensor
+        let q = qkv.narrow(1, 0, self.num_attention_heads)?;
+        let k = qkv.narrow(1, self.num_attention_heads, self.num_attention_heads)?;
+        let v = qkv.narrow(1, self.num_attention_heads * 2, self.num_attention_heads)?;
+
+        candle_rotary::apply_rotary_inplace(&q, &k, &cos, &sin, true)?;
+
+        let attention = flash_attn_varlen(
+            &q,
+            &k,
+            &v,
+            None,
+            cu_seqlens,
+            cu_seqlens,
+            max_s,
+            max_s,
+            self.softmax_scale,
+            false,
+            None,
+        )?;
+        let attention = attention.flatten_from(candle::D::Minus2)?;
+
+        self.o_proj.forward(&attention)
+    }
+}
+
+struct GTEMLP {
+    up_gate_proj: Linear,
+    down_proj: Linear,
+
+    act: HiddenAct,
+    intermediate_size: usize,
+
+    span: tracing::Span,
+}
+
+impl GTEMLP {
+    pub fn load(vb: VarBuilder, config: &GTEConfig) -> Result<Self> {
+        let intermediate_size = config.intermediate_size;
+
+        let up_gate_proj_weight = vb
+            .pp("up_gate_proj")
+            .get((intermediate_size * 2, config.hidden_size), "weight")?;
+
+        let up_gate_proj = Linear::new(up_gate_proj_weight, None, None);
+
+        let down_proj_weight = vb
+            .pp("down_proj")
+            .get((config.hidden_size, intermediate_size), "weight")?;
+        let down_proj_bias = vb.pp("down_proj").get(config.hidden_size, "bias")?;
+        let down_proj = Linear::new(down_proj_weight, Some(down_proj_bias), None);
+
+        Ok(Self {
+            up_gate_proj,
+            down_proj,
+            intermediate_size,
+            act: config.hidden_act.clone(),
+            span: tracing::span!(tracing::Level::TRACE, "mlp"),
+        })
+    }
+
+    pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let up_gate_states = self.up_gate_proj.forward(hidden_states)?;
+        let up_states = up_gate_states.narrow(1, 0, self.intermediate_size)?;
+        let gate_states =
+            up_gate_states.narrow(1, self.intermediate_size, self.intermediate_size)?;
+
+        let gate_states = match self.act {
+            HiddenAct::Gelu => gate_states.gelu(),
+            HiddenAct::Relu => gate_states.relu(),
+            HiddenAct::Swiglu => gate_states.silu(),
+        }?;
+        let r = self.down_proj.forward(&(gate_states * up_states)?);
+        r
+    }
+}
+
+struct GTELayer {
+    attention: GTEAttention,
+    mlp: GTEMLP,
+    attention_layer_norm: LayerNorm,
+    mlp_layer_norm: LayerNorm,
+
+    span: tracing::Span,
+}
+
+impl GTELayer {
+    pub fn load(vb: VarBuilder, config: &GTEConfig) -> Result<Self> {
+        let attention = GTEAttention::load(vb.pp("attention"), config)?;
+        let mlp = GTEMLP::load(vb.pp("mlp"), config)?;
+
+        let attention_layer_norm =
+            LayerNorm::load(vb.pp("attn_ln"), config.hidden_size, config.layer_norm_eps)?;
+        let mlp_layer_norm =
+            LayerNorm::load(vb.pp("mlp_ln"), config.hidden_size, config.layer_norm_eps)?;
+
+        Ok(Self {
+            attention,
+            mlp,
+            attention_layer_norm,
+            mlp_layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "layer"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let attn_output = self
+            .attention
+            .forward(&hidden_states, cu_seqlens, cos, sin, max_s)?;
+        let normed_attn_res_output = self
+            .attention_layer_norm
+            .forward(&attn_output, Some(hidden_states))?;
+
+        let mlp_output = self.mlp.forward(&normed_attn_res_output)?;
+        let normed_mlp_res_output = self
+            .mlp_layer_norm
+            .forward(&mlp_output, Some(&normed_attn_res_output))?;
+        Ok(normed_mlp_res_output)
+    }
+}
+
+pub struct FlashGTEModel {
+    word_embeddings: Embedding,
+    token_type_embeddings: Option<Embedding>,
+    layers: Vec<GTELayer>,
+    embeddings_norm: LayerNorm,
+    cos_cache: Tensor,
+    sin_cache: Tensor,
+    pool: Pool,
+    pub device: Device,
+
+    span: tracing::Span,
+}
+
+impl FlashGTEModel {
+    pub fn load(vb: VarBuilder, config: &GTEConfig, model_type: ModelType) -> Result<Self> {
+        match vb.device() {
+            Device::Cuda(_) => {}
+            _ => candle::bail!("FlashGTE requires Cuda"),
+        }
+
+        if vb.dtype() != DType::F16 {
+            candle::bail!("FlashGTE requires DType::F16")
+        }
+
+        if config.logn_attention_clip1 {
+            candle::bail!("`logn_attention_clip1` is not supported");
+        }
+        if config.logn_attention_scale {
+            candle::bail!("`logn_attention_scale` is not supported");
+        }
+
+        if config.position_embedding_type != PositionEmbeddingType::Rope {
+            candle::bail!("Only `PositionEmbeddingType::Rope` is supported");
+        }
+
+        let pool = match model_type {
+            ModelType::Classifier => {
+                candle::bail!("`classifier` model type is not supported for GTE")
+            }
+            ModelType::Embedding(pool) => pool,
+        };
+
+        let word_embeddings = Embedding::new(
+            vb.pp("embeddings.word_embeddings")
+                .get((config.vocab_size, config.hidden_size), "weight")?,
+            config.hidden_size,
+        );
+
+        let token_type_embeddings = if config.type_vocab_size > 0 {
+            Some(Embedding::new(
+                vb.pp("embeddings.token_type_embeddings")
+                    .get((config.type_vocab_size, config.hidden_size), "weight")?,
+                config.hidden_size,
+            ))
+        } else {
+            None
+        };
+
+        let layers = (0..config.num_hidden_layers)
+            .map(|index| GTELayer::load(vb.pp(format!("encoder.layer.{index}")), config))
+            .collect::<Result<Vec<_>>>()?;
+
+        let embeddings_norm = LayerNorm::load(
+            vb.pp("embeddings.LayerNorm"),
+            config.hidden_size,
+            config.layer_norm_eps,
+        )?;
+
+        let inv_freqs = if let Some(RopeScaling::Ntk(NTKScaling { factor })) = config.rope_scaling {
+            let inv_freqs = candle_rotary::inv_freqs(
+                layers[0].attention.attention_head_size,
+                config.rope_theta * factor,
+                vb.device(),
+            )?;
+            let s = factor.powf(2.0 / layers[0].attention.attention_head_size as f32) as f64;
+            inv_freqs / s
+        } else {
+            candle_rotary::inv_freqs(
+                layers[0].attention.attention_head_size,
+                config.rope_theta,
+                vb.device(),
+            )
+        }?;
+
+        let (cos_cache, sin_cache) =
+            candle_rotary::cos_sin(config.max_position_embeddings, &inv_freqs, vb.dtype())?;
+
+        Ok(Self {
+            word_embeddings,
+            token_type_embeddings,
+            layers,
+            embeddings_norm,
+            cos_cache,
+            sin_cache,
+            pool,
+            device: vb.device().clone(),
+            span: tracing::span!(tracing::Level::TRACE, "model"),
+        })
+    }
+
+    pub fn forward(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        let _enter = self.span.enter();
+
+        let batch_size = batch.cumulative_seq_lengths.len() - 1;
+        let shape = batch.input_ids.len();
+
+        // Create Cuda tensors
+        let input_ids = Tensor::from_vec(batch.input_ids, shape, &self.device)?;
+        let token_type_ids = Tensor::from_vec(batch.token_type_ids, shape, &self.device)?;
+        let position_ids = Tensor::from_vec(batch.position_ids, shape, &self.device)?;
+        let cu_seqlens = Tensor::from_vec(
+            batch.cumulative_seq_lengths.clone(),
+            batch_size + 1,
+            &self.device,
+        )?;
+
+        let word_embeddings = self.word_embeddings.forward(&input_ids)?;
+        let token_type_embeddings = self
+            .token_type_embeddings
+            .as_ref()
+            .map(|emb| emb.forward(&token_type_ids))
+            .transpose()?;
+
+        let mut hidden_states = self
+            .embeddings_norm
+            .forward(&word_embeddings, token_type_embeddings.as_ref())?;
+
+        let cos = self.cos_cache.index_select(&position_ids, 0)?;
+        let sin = self.sin_cache.index_select(&position_ids, 0)?;
+
+        for layer in &self.layers {
+            let h = layer.forward(
+                &hidden_states,
+                &cu_seqlens,
+                &cos,
+                &sin,
+                batch.max_length as usize,
+            )?;
+            hidden_states = h;
+        }
+
+        let outputs = hidden_states;
+
+        let has_pooling_requests = !batch.pooled_indices.is_empty();
+        let has_raw_requests = !batch.raw_indices.is_empty();
+
+        let pooled_embeddings = if has_pooling_requests {
+            match self.pool {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
+                    if batch_size > 1 {
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
+
+                        // If raw_indices is empty, we don't need to do anything with
+                        // the pooled_indices
+                        if has_raw_requests {
+                            // We need the pooled indices to select the correct cls indices
+                            let pooled_indices = Tensor::from_vec(
+                                batch.pooled_indices.clone(),
+                                batch.pooled_indices.len(),
+                                &self.device,
+                            )?;
+
+                            // Only select indices that requires pooling
+                            indices = indices.index_select(&pooled_indices, 0)?
+                        }
+
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
+                    } else {
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
+                    }
+                }
+                // Mean pooling
+                Pool::Mean => {
+                    if batch_size > 1 {
+                        // for each request that requires pooling
+                        let results: Result<Vec<Tensor>> = batch
+                            .pooled_indices
+                            .into_iter()
+                            .map(|i| {
+                                let i = i as usize;
+                                let start = batch.cumulative_seq_lengths[i];
+                                let len = batch.cumulative_seq_lengths[i + 1] - start;
+
+                                // Mean
+                                let embeddings = outputs.narrow(0, start as usize, len as usize)?;
+                                embeddings.sum_keepdim(0)? / (len as f64)
+                            })
+                            .collect();
+
+                        // Concatenate all results
+                        Some(Tensor::cat(&results?, 0)?)
+                    } else {
+                        Some((outputs.sum_keepdim(0)? / (batch.max_length as f64))?)
+                    }
+                }
+                Pool::Splade => {
+                    unreachable!();
+                }
+            }
+        } else {
+            None
+        };
+
+        let raw_embeddings = if has_raw_requests {
+            if batch_size > 1 && has_pooling_requests {
+                // Create indexing vector for the embeddings
+                let mut final_indices: Vec<u32> = Vec::with_capacity(shape);
+                for i in batch.raw_indices.into_iter() {
+                    let i = i as usize;
+                    // Get start/end token index of this specific member of the batch
+                    let start = batch.cumulative_seq_lengths[i];
+                    let end = batch.cumulative_seq_lengths[i + 1];
+
+                    for j in start..end {
+                        // Add indices for the tokens of this specific member of the batch
+                        final_indices.push(j);
+                    }
+                }
+
+                let final_indices_length = final_indices.len();
+                let final_indices =
+                    Tensor::from_vec(final_indices, final_indices_length, &self.device)?;
+
+                // Select the tokens with final indices
+                Some(outputs.index_select(&final_indices, 0)?)
+            } else {
+                Some(outputs)
+            }
+        } else {
+            None
+        };
+
+        Ok((pooled_embeddings, raw_embeddings))
+    }
+}
+
+impl Model for FlashGTEModel {
+    fn is_padded(&self) -> bool {
+        false
+    }
+    fn embed(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        self.forward(batch)
+    }
+}
diff --git a/backends/candle/src/models/flash_jina.rs b/backends/candle/src/models/flash_jina.rs
index b2d47e51..83c5b0a4 100644
--- a/backends/candle/src/models/flash_jina.rs
+++ b/backends/candle/src/models/flash_jina.rs
@@ -105,6 +105,7 @@ impl JinaAttention {
             max_s,
             self.softmax_scale,
             false,
+            None,
         )?;
         let attention = attention.flatten_from(candle::D::Minus2)?;
 
@@ -241,6 +242,7 @@ impl FlashJinaBertModel {
                 )
             }
             PositionEmbeddingType::Absolute => None,
+            _ => candle::bail!("not supported"),
         };
 
         match vb.device() {
@@ -319,11 +321,18 @@ impl FlashJinaBertModel {
 
         let pooled_embeddings = if has_pooling_requests {
             match self.pool {
-                // CLS pooling
-                Pool::Cls => {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
                     if batch_size > 1 {
-                        // Get the indices of the cls tokens from cu_seqlens
-                        let mut cls_indices = cu_seqlens.narrow(0, 0, batch_size)?;
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
 
                         // If raw_indices is empty, we don't need to do anything with
                         // the pooled_indices
@@ -336,13 +345,22 @@ impl FlashJinaBertModel {
                             )?;
 
                             // Only select indices that requires pooling
-                            cls_indices = cls_indices.index_select(&pooled_indices, 0)?
+                            indices = indices.index_select(&pooled_indices, 0)?
                         }
 
-                        // Select cls tokens
-                        Some(outputs.index_select(&cls_indices, 0)?)
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
                     } else {
-                        Some(outputs.i(0)?)
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
                     }
                 }
                 // Mean pooling
diff --git a/backends/candle/src/models/flash_jina_code.rs b/backends/candle/src/models/flash_jina_code.rs
index 5df80be2..56ab1976 100644
--- a/backends/candle/src/models/flash_jina_code.rs
+++ b/backends/candle/src/models/flash_jina_code.rs
@@ -141,6 +141,7 @@ impl JinaCodeAttention {
             max_s,
             self.softmax_scale,
             false,
+            None,
         )?;
         let attention = attention.flatten_from(candle::D::Minus2)?;
 
@@ -294,6 +295,7 @@ impl FlashJinaCodeBertModel {
                 )
             }
             PositionEmbeddingType::Absolute => None,
+            _ => candle::bail!("not supported"),
         };
 
         match vb.device() {
@@ -372,11 +374,18 @@ impl FlashJinaCodeBertModel {
 
         let pooled_embeddings = if has_pooling_requests {
             match self.pool {
-                // CLS pooling
-                Pool::Cls => {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
                     if batch_size > 1 {
-                        // Get the indices of the cls tokens from cu_seqlens
-                        let mut cls_indices = cu_seqlens.narrow(0, 0, batch_size)?;
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
 
                         // If raw_indices is empty, we don't need to do anything with
                         // the pooled_indices
@@ -389,13 +398,22 @@ impl FlashJinaCodeBertModel {
                             )?;
 
                             // Only select indices that requires pooling
-                            cls_indices = cls_indices.index_select(&pooled_indices, 0)?
+                            indices = indices.index_select(&pooled_indices, 0)?
                         }
 
-                        // Select cls tokens
-                        Some(outputs.index_select(&cls_indices, 0)?)
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
                     } else {
-                        Some(outputs.i(0)?)
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
                     }
                 }
                 // Mean pooling
diff --git a/backends/candle/src/models/flash_mistral.rs b/backends/candle/src/models/flash_mistral.rs
new file mode 100644
index 00000000..eb94c913
--- /dev/null
+++ b/backends/candle/src/models/flash_mistral.rs
@@ -0,0 +1,445 @@
+use crate::flash_attn::flash_attn_varlen;
+use crate::layers::{HiddenAct, Linear, RMSNorm};
+use crate::models::{MistralConfig, Model};
+use candle::{DType, Device, IndexOp, Result, Tensor};
+use candle_nn::{Embedding, Module, VarBuilder};
+use text_embeddings_backend_core::{Batch, ModelType, Pool};
+
+struct MistralAttention {
+    qkv_linear: Linear,
+    o_proj: Linear,
+
+    window_size_left: Option<usize>,
+
+    num_attention_heads: usize,
+    num_key_value_heads: usize,
+    attention_head_size: usize,
+
+    softmax_scale: f32,
+
+    span: tracing::Span,
+}
+
+impl MistralAttention {
+    pub fn load(vb: VarBuilder, config: &MistralConfig) -> Result<Self> {
+        let window_size_left = config.sliding_window;
+        let num_attention_heads = config.num_attention_heads;
+        let attention_head_size = config.hidden_size / config.num_attention_heads;
+        let num_key_value_heads = config.num_key_value_heads;
+        let hidden_size = config.hidden_size;
+
+        let query_weight = vb.pp("q_proj").get((hidden_size, hidden_size), "weight")?;
+
+        let key_weight = vb.pp("k_proj").get(
+            (num_key_value_heads * attention_head_size, hidden_size),
+            "weight",
+        )?;
+
+        let value_weight = vb.pp("v_proj").get(
+            (num_key_value_heads * attention_head_size, hidden_size),
+            "weight",
+        )?;
+
+        let qkv_weight = Tensor::cat(&[&query_weight, &key_weight, &value_weight], 0)?;
+        let qkv_linear = Linear::new(qkv_weight, None, None);
+
+        let o_proj_weight = vb.pp("o_proj").get((hidden_size, hidden_size), "weight")?;
+
+        let o_proj = Linear::new(o_proj_weight, None, None);
+
+        let softmax_scale = (1. / (attention_head_size as f64).sqrt()) as f32;
+
+        Ok(Self {
+            qkv_linear,
+            o_proj,
+            window_size_left,
+            num_attention_heads,
+            num_key_value_heads,
+            attention_head_size,
+            softmax_scale,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let qkv = self.qkv_linear.forward(hidden_states)?;
+
+        // Reshape to [tokens, heads, head_size]
+        let mut new_qkv_shape = qkv.dims().to_vec();
+        new_qkv_shape.pop();
+        new_qkv_shape.push(self.num_attention_heads + 2 * self.num_key_value_heads);
+        new_qkv_shape.push(self.attention_head_size);
+
+        let qkv = qkv.reshape(new_qkv_shape)?;
+
+        // Split qkv tensor
+        let q = qkv.narrow(1, 0, self.num_attention_heads)?;
+        let k = qkv.narrow(1, self.num_attention_heads, self.num_key_value_heads)?;
+        let v = qkv.narrow(
+            1,
+            self.num_attention_heads + self.num_key_value_heads,
+            self.num_key_value_heads,
+        )?;
+
+        candle_rotary::apply_rotary_inplace(&q, &k, &cos, &sin, true)?;
+
+        let attention = flash_attn_varlen(
+            &q,
+            &k,
+            &v,
+            None,
+            cu_seqlens,
+            cu_seqlens,
+            max_s,
+            max_s,
+            self.softmax_scale,
+            true,
+            self.window_size_left,
+        )?;
+        let attention = attention.flatten_from(candle::D::Minus2)?;
+
+        self.o_proj.forward(&attention)
+    }
+}
+
+struct MistralMLP {
+    gate_up_proj: Linear,
+    down_proj: Linear,
+
+    act: HiddenAct,
+    intermediate_size: usize,
+
+    span: tracing::Span,
+}
+
+impl MistralMLP {
+    pub fn load(vb: VarBuilder, config: &MistralConfig) -> Result<Self> {
+        let intermediate_size = config.intermediate_size;
+
+        let gate_proj_weight = vb
+            .pp("gate_proj")
+            .get((intermediate_size, config.hidden_size), "weight")?;
+
+        let up_proj_weight = vb
+            .pp("up_proj")
+            .get((intermediate_size, config.hidden_size), "weight")?;
+
+        let gate_up_proj_weight = Tensor::cat(&[&gate_proj_weight, &up_proj_weight], 0)?;
+        let gate_up_proj = Linear::new(gate_up_proj_weight, None, None);
+
+        let down_proj_weight = vb
+            .pp("down_proj")
+            .get((config.hidden_size, intermediate_size), "weight")?;
+        let down_proj = Linear::new(down_proj_weight, None, None);
+
+        Ok(Self {
+            gate_up_proj,
+            down_proj,
+            intermediate_size,
+            act: config.hidden_act.clone(),
+            span: tracing::span!(tracing::Level::TRACE, "mlp"),
+        })
+    }
+
+    pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let gate_up_states = self.gate_up_proj.forward(hidden_states)?;
+        let gate_states = gate_up_states.narrow(1, 0, self.intermediate_size)?;
+        let up_states = gate_up_states.narrow(1, self.intermediate_size, self.intermediate_size)?;
+
+        let gate_states = match self.act {
+            HiddenAct::Gelu => gate_states.gelu(),
+            HiddenAct::Relu => gate_states.relu(),
+            HiddenAct::Swiglu => gate_states.silu(),
+        }?;
+        let r = self.down_proj.forward(&(gate_states * up_states)?);
+        r
+    }
+}
+
+struct MistralLayer {
+    attention: MistralAttention,
+    mlp: MistralMLP,
+    input_layer_norm: RMSNorm,
+    post_attention_layer_norm: RMSNorm,
+
+    span: tracing::Span,
+}
+
+impl MistralLayer {
+    pub fn load(vb: VarBuilder, config: &MistralConfig) -> Result<Self> {
+        let attention = MistralAttention::load(vb.pp("self_attn"), config)?;
+        let mlp = MistralMLP::load(vb.pp("mlp"), config)?;
+
+        let input_layer_norm = RMSNorm::load(
+            vb.pp("input_layernorm"),
+            config.hidden_size,
+            config.rms_norm_eps,
+        )?;
+        let post_attention_layer_norm = RMSNorm::load(
+            vb.pp("post_attention_layernorm"),
+            config.hidden_size,
+            config.rms_norm_eps,
+        )?;
+
+        Ok(Self {
+            attention,
+            mlp,
+            input_layer_norm,
+            post_attention_layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "layer"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        residual: Option<&Tensor>,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<(Tensor, Tensor)> {
+        let _enter = self.span.enter();
+
+        let (normed_hidden_states, res) = self.input_layer_norm.forward(hidden_states, residual)?;
+        let attn_output =
+            self.attention
+                .forward(&normed_hidden_states, cu_seqlens, cos, sin, max_s)?;
+        let (normed_attn_res_output, attn_res) = self
+            .post_attention_layer_norm
+            .forward(&attn_output, Some(&res))?;
+        let mlp_output = self.mlp.forward(&normed_attn_res_output)?;
+
+        Ok((mlp_output, attn_res))
+    }
+}
+
+pub struct FlashMistralModel {
+    embeddings: Embedding,
+    layers: Vec<MistralLayer>,
+    norm: RMSNorm,
+    cos_cache: Tensor,
+    sin_cache: Tensor,
+    pool: Pool,
+    pub device: Device,
+
+    span: tracing::Span,
+}
+
+impl FlashMistralModel {
+    pub fn load(vb: VarBuilder, config: &MistralConfig, model_type: ModelType) -> Result<Self> {
+        match vb.device() {
+            Device::Cuda(_) => {}
+            _ => candle::bail!("FlashMistral requires Cuda"),
+        }
+
+        if vb.dtype() != DType::F16 {
+            candle::bail!("FlashMistral requires DType::F16")
+        }
+
+        let pool = match model_type {
+            ModelType::Classifier => {
+                candle::bail!("`classifier` model type is not supported for Mistral")
+            }
+            ModelType::Embedding(pool) => pool,
+        };
+
+        let embeddings = Embedding::new(
+            vb.pp("embed_tokens")
+                .get((config.vocab_size, config.hidden_size), "weight")?,
+            config.hidden_size,
+        );
+
+        let layers = (0..config.num_hidden_layers)
+            .map(|index| MistralLayer::load(vb.pp(format!("layers.{index}")), config))
+            .collect::<Result<Vec<_>>>()?;
+
+        let norm = RMSNorm::load(vb.pp("norm"), config.hidden_size, config.rms_norm_eps)?;
+
+        let inv_freqs = candle_rotary::inv_freqs(
+            layers[0].attention.attention_head_size,
+            config.rope_theta,
+            vb.device(),
+        )?;
+        let (cos_cache, sin_cache) =
+            candle_rotary::cos_sin(config.max_position_embeddings, &inv_freqs, vb.dtype())?;
+
+        Ok(Self {
+            embeddings,
+            layers,
+            norm,
+            cos_cache,
+            sin_cache,
+            pool,
+            device: vb.device().clone(),
+            span: tracing::span!(tracing::Level::TRACE, "model"),
+        })
+    }
+
+    pub fn forward(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        let _enter = self.span.enter();
+
+        let batch_size = batch.cumulative_seq_lengths.len() - 1;
+        let shape = batch.input_ids.len();
+
+        // Create Cuda tensors
+        let input_ids = Tensor::from_vec(batch.input_ids, shape, &self.device)?;
+        let position_ids = Tensor::from_vec(batch.position_ids, shape, &self.device)?;
+        let cu_seqlens = Tensor::from_vec(
+            batch.cumulative_seq_lengths.clone(),
+            batch_size + 1,
+            &self.device,
+        )?;
+
+        let mut hidden_states = self.embeddings.forward(&input_ids)?;
+
+        let cos = self.cos_cache.index_select(&position_ids, 0)?;
+        let sin = self.sin_cache.index_select(&position_ids, 0)?;
+
+        let mut residual = None;
+        for layer in &self.layers {
+            let (h, r) = layer.forward(
+                &hidden_states,
+                residual.as_ref(),
+                &cu_seqlens,
+                &cos,
+                &sin,
+                batch.max_length as usize,
+            )?;
+            hidden_states = h;
+            residual = Some(r);
+        }
+
+        let (outputs, _) = self.norm.forward(&hidden_states, residual.as_ref())?;
+
+        let has_pooling_requests = !batch.pooled_indices.is_empty();
+        let has_raw_requests = !batch.raw_indices.is_empty();
+
+        let pooled_embeddings = if has_pooling_requests {
+            match self.pool {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
+                    if batch_size > 1 {
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
+
+                        // If raw_indices is empty, we don't need to do anything with
+                        // the pooled_indices
+                        if has_raw_requests {
+                            // We need the pooled indices to select the correct cls indices
+                            let pooled_indices = Tensor::from_vec(
+                                batch.pooled_indices.clone(),
+                                batch.pooled_indices.len(),
+                                &self.device,
+                            )?;
+
+                            // Only select indices that requires pooling
+                            indices = indices.index_select(&pooled_indices, 0)?
+                        }
+
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
+                    } else {
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
+                    }
+                }
+                // Mean pooling
+                Pool::Mean => {
+                    if batch_size > 1 {
+                        // for each request that requires pooling
+                        let results: Result<Vec<Tensor>> = batch
+                            .pooled_indices
+                            .into_iter()
+                            .map(|i| {
+                                let i = i as usize;
+                                let start = batch.cumulative_seq_lengths[i];
+                                let len = batch.cumulative_seq_lengths[i + 1] - start;
+
+                                // Mean
+                                let embeddings = outputs.narrow(0, start as usize, len as usize)?;
+                                embeddings.sum_keepdim(0)? / (len as f64)
+                            })
+                            .collect();
+
+                        // Concatenate all results
+                        Some(Tensor::cat(&results?, 0)?)
+                    } else {
+                        Some((outputs.sum_keepdim(0)? / (batch.max_length as f64))?)
+                    }
+                }
+                Pool::Splade => {
+                    unreachable!();
+                }
+            }
+        } else {
+            None
+        };
+
+        let raw_embeddings = if has_raw_requests {
+            if batch_size > 1 && has_pooling_requests {
+                // Create indexing vector for the embeddings
+                let mut final_indices: Vec<u32> = Vec::with_capacity(shape);
+                for i in batch.raw_indices.into_iter() {
+                    let i = i as usize;
+                    // Get start/end token index of this specific member of the batch
+                    let start = batch.cumulative_seq_lengths[i];
+                    let end = batch.cumulative_seq_lengths[i + 1];
+
+                    for j in start..end {
+                        // Add indices for the tokens of this specific member of the batch
+                        final_indices.push(j);
+                    }
+                }
+
+                let final_indices_length = final_indices.len();
+                let final_indices =
+                    Tensor::from_vec(final_indices, final_indices_length, &self.device)?;
+
+                // Select the tokens with final indices
+                Some(outputs.index_select(&final_indices, 0)?)
+            } else {
+                Some(outputs)
+            }
+        } else {
+            None
+        };
+
+        Ok((pooled_embeddings, raw_embeddings))
+    }
+}
+
+impl Model for FlashMistralModel {
+    fn is_padded(&self) -> bool {
+        false
+    }
+    fn embed(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        self.forward(batch)
+    }
+}
diff --git a/backends/candle/src/models/flash_nomic.rs b/backends/candle/src/models/flash_nomic.rs
index 12eff0f1..7f558b9e 100644
--- a/backends/candle/src/models/flash_nomic.rs
+++ b/backends/candle/src/models/flash_nomic.rs
@@ -81,6 +81,7 @@ impl NomicAttention {
             max_s,
             self.softmax_scale,
             false,
+            None,
         )?;
         let attention = attention.flatten_from(D::Minus2)?;
 
@@ -304,11 +305,18 @@ impl FlashNomicBertModel {
 
         let pooled_embeddings = if has_pooling_requests {
             match self.pool {
-                // CLS pooling
-                Pool::Cls => {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
                     if batch_size > 1 {
-                        // Get the indices of the cls tokens from cu_seqlens
-                        let mut cls_indices = cu_seqlens.narrow(0, 0, batch_size)?;
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
 
                         // If raw_indices is empty, we don't need to do anything with
                         // the pooled_indices
@@ -321,13 +329,22 @@ impl FlashNomicBertModel {
                             )?;
 
                             // Only select indices that requires pooling
-                            cls_indices = cls_indices.index_select(&pooled_indices, 0)?
+                            indices = indices.index_select(&pooled_indices, 0)?
                         }
 
-                        // Select cls tokens
-                        Some(outputs.index_select(&cls_indices, 0)?)
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
                     } else {
-                        Some(outputs.i(0)?)
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
                     }
                 }
                 // Mean pooling
diff --git a/backends/candle/src/models/flash_qwen2.rs b/backends/candle/src/models/flash_qwen2.rs
new file mode 100644
index 00000000..4dba7868
--- /dev/null
+++ b/backends/candle/src/models/flash_qwen2.rs
@@ -0,0 +1,455 @@
+use crate::flash_attn::flash_attn_varlen;
+use crate::layers::{HiddenAct, Linear, RMSNorm};
+use crate::models::{Model, Qwen2Config};
+use candle::{DType, Device, IndexOp, Result, Tensor};
+use candle_nn::{Embedding, Module, VarBuilder};
+use text_embeddings_backend_core::{Batch, ModelType, Pool};
+
+struct Qwen2Attention {
+    qkv_linear: Linear,
+    o_proj: Linear,
+
+    num_attention_heads: usize,
+    num_key_value_heads: usize,
+    attention_head_size: usize,
+
+    softmax_scale: f32,
+
+    span: tracing::Span,
+}
+
+impl Qwen2Attention {
+    pub fn load(vb: VarBuilder, config: &Qwen2Config) -> Result<Self> {
+        if config.use_sliding_window {
+            candle::bail!("Sliding window is not supported");
+        }
+
+        let num_attention_heads = config.num_attention_heads;
+        let attention_head_size = config.hidden_size / config.num_attention_heads;
+        let num_key_value_heads = config.num_key_value_heads;
+        let hidden_size = config.hidden_size;
+
+        let query_weight = vb.pp("q_proj").get((hidden_size, hidden_size), "weight")?;
+        let query_bias = vb.pp("q_proj").get(hidden_size, "bias")?;
+
+        let key_weight = vb.pp("k_proj").get(
+            (num_key_value_heads * attention_head_size, hidden_size),
+            "weight",
+        )?;
+        let key_bias = vb
+            .pp("k_proj")
+            .get(num_key_value_heads * attention_head_size, "bias")?;
+
+        let value_weight = vb.pp("v_proj").get(
+            (num_key_value_heads * attention_head_size, hidden_size),
+            "weight",
+        )?;
+        let value_bias = vb
+            .pp("v_proj")
+            .get(num_key_value_heads * attention_head_size, "bias")?;
+
+        let qkv_weight = Tensor::cat(&[&query_weight, &key_weight, &value_weight], 0)?;
+        let qkv_bias = Tensor::cat(&[&query_bias, &key_bias, &value_bias], 0)?;
+        let qkv_linear = Linear::new(qkv_weight, Some(qkv_bias), None);
+
+        let o_proj_weight = vb.pp("o_proj").get((hidden_size, hidden_size), "weight")?;
+
+        let o_proj = Linear::new(o_proj_weight, None, None);
+
+        let softmax_scale = (1. / (attention_head_size as f64).sqrt()) as f32;
+
+        Ok(Self {
+            qkv_linear,
+            o_proj,
+            num_attention_heads,
+            num_key_value_heads,
+            attention_head_size,
+            softmax_scale,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let qkv = self.qkv_linear.forward(hidden_states)?;
+
+        // Reshape to [tokens, heads, head_size]
+        let mut new_qkv_shape = qkv.dims().to_vec();
+        new_qkv_shape.pop();
+        new_qkv_shape.push(self.num_attention_heads + 2 * self.num_key_value_heads);
+        new_qkv_shape.push(self.attention_head_size);
+
+        let qkv = qkv.reshape(new_qkv_shape)?;
+
+        // Split qkv tensor
+        let q = qkv.narrow(1, 0, self.num_attention_heads)?;
+        let k = qkv.narrow(1, self.num_attention_heads, self.num_key_value_heads)?;
+        let v = qkv.narrow(
+            1,
+            self.num_attention_heads + self.num_key_value_heads,
+            self.num_key_value_heads,
+        )?;
+
+        candle_rotary::apply_rotary_inplace(&q, &k, &cos, &sin, true)?;
+
+        let attention = flash_attn_varlen(
+            &q,
+            &k,
+            &v,
+            None,
+            cu_seqlens,
+            cu_seqlens,
+            max_s,
+            max_s,
+            self.softmax_scale,
+            false,
+            None,
+        )?;
+        let attention = attention.flatten_from(candle::D::Minus2)?;
+
+        self.o_proj.forward(&attention)
+    }
+}
+
+struct Qwen2MLP {
+    gate_up_proj: Linear,
+    down_proj: Linear,
+
+    act: HiddenAct,
+    intermediate_size: usize,
+
+    span: tracing::Span,
+}
+
+impl Qwen2MLP {
+    pub fn load(vb: VarBuilder, config: &Qwen2Config) -> Result<Self> {
+        let intermediate_size = config.intermediate_size;
+
+        let gate_proj_weight = vb
+            .pp("gate_proj")
+            .get((intermediate_size, config.hidden_size), "weight")?;
+
+        let up_proj_weight = vb
+            .pp("up_proj")
+            .get((intermediate_size, config.hidden_size), "weight")?;
+
+        let gate_up_proj_weight = Tensor::cat(&[&gate_proj_weight, &up_proj_weight], 0)?;
+        let gate_up_proj = Linear::new(gate_up_proj_weight, None, None);
+
+        let down_proj_weight = vb
+            .pp("down_proj")
+            .get((config.hidden_size, intermediate_size), "weight")?;
+        let down_proj = Linear::new(down_proj_weight, None, None);
+
+        Ok(Self {
+            gate_up_proj,
+            down_proj,
+            intermediate_size,
+            act: config.hidden_act.clone(),
+            span: tracing::span!(tracing::Level::TRACE, "mlp"),
+        })
+    }
+
+    pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let gate_up_states = self.gate_up_proj.forward(hidden_states)?;
+        let gate_states = gate_up_states.narrow(1, 0, self.intermediate_size)?;
+        let up_states = gate_up_states.narrow(1, self.intermediate_size, self.intermediate_size)?;
+
+        let gate_states = match self.act {
+            HiddenAct::Gelu => gate_states.gelu(),
+            HiddenAct::Relu => gate_states.relu(),
+            HiddenAct::Swiglu => gate_states.silu(),
+        }?;
+        let r = self.down_proj.forward(&(gate_states * up_states)?);
+        r
+    }
+}
+
+struct Qwen2Layer {
+    attention: Qwen2Attention,
+    mlp: Qwen2MLP,
+    input_layer_norm: RMSNorm,
+    post_attention_layer_norm: RMSNorm,
+
+    span: tracing::Span,
+}
+
+impl Qwen2Layer {
+    pub fn load(vb: VarBuilder, config: &Qwen2Config) -> Result<Self> {
+        let attention = Qwen2Attention::load(vb.pp("self_attn"), config)?;
+        let mlp = Qwen2MLP::load(vb.pp("mlp"), config)?;
+
+        let input_layer_norm = RMSNorm::load(
+            vb.pp("input_layernorm"),
+            config.hidden_size,
+            config.rms_norm_eps,
+        )?;
+        let post_attention_layer_norm = RMSNorm::load(
+            vb.pp("post_attention_layernorm"),
+            config.hidden_size,
+            config.rms_norm_eps,
+        )?;
+
+        Ok(Self {
+            attention,
+            mlp,
+            input_layer_norm,
+            post_attention_layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "layer"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        residual: Option<&Tensor>,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<(Tensor, Tensor)> {
+        let _enter = self.span.enter();
+
+        let (normed_hidden_states, res) = self.input_layer_norm.forward(hidden_states, residual)?;
+        let attn_output =
+            self.attention
+                .forward(&normed_hidden_states, cu_seqlens, cos, sin, max_s)?;
+        let (normed_attn_res_output, attn_res) = self
+            .post_attention_layer_norm
+            .forward(&attn_output, Some(&res))?;
+        let mlp_output = self.mlp.forward(&normed_attn_res_output)?;
+
+        Ok((mlp_output, attn_res))
+    }
+}
+
+pub struct FlashQwen2Model {
+    embeddings: Embedding,
+    layers: Vec<Qwen2Layer>,
+    norm: RMSNorm,
+    cos_cache: Tensor,
+    sin_cache: Tensor,
+    pool: Pool,
+    pub device: Device,
+
+    span: tracing::Span,
+}
+
+impl FlashQwen2Model {
+    pub fn load(vb: VarBuilder, config: &Qwen2Config, model_type: ModelType) -> Result<Self> {
+        match vb.device() {
+            Device::Cuda(_) => {}
+            _ => candle::bail!("FlashQwen2 requires Cuda"),
+        }
+
+        if vb.dtype() != DType::F16 {
+            candle::bail!("FlashQwen2 requires DType::F16")
+        }
+
+        let pool = match model_type {
+            ModelType::Classifier => {
+                candle::bail!("`classifier` model type is not supported for Qwen2")
+            }
+            ModelType::Embedding(pool) => pool,
+        };
+
+        let vb = vb.pp("model");
+
+        let embeddings = Embedding::new(
+            vb.pp("embed_tokens")
+                .get((config.vocab_size, config.hidden_size), "weight")?,
+            config.hidden_size,
+        );
+
+        let layers = (0..config.num_hidden_layers)
+            .map(|index| Qwen2Layer::load(vb.pp(format!("layers.{index}")), config))
+            .collect::<Result<Vec<_>>>()?;
+
+        let norm = RMSNorm::load(vb.pp("norm"), config.hidden_size, config.rms_norm_eps)?;
+
+        let inv_freqs = candle_rotary::inv_freqs(
+            layers[0].attention.attention_head_size,
+            config.rope_theta,
+            vb.device(),
+        )?;
+        let (cos_cache, sin_cache) =
+            candle_rotary::cos_sin(config.max_position_embeddings, &inv_freqs, vb.dtype())?;
+
+        Ok(Self {
+            embeddings,
+            layers,
+            norm,
+            cos_cache,
+            sin_cache,
+            pool,
+            device: vb.device().clone(),
+            span: tracing::span!(tracing::Level::TRACE, "model"),
+        })
+    }
+
+    pub fn forward(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        let _enter = self.span.enter();
+
+        let batch_size = batch.cumulative_seq_lengths.len() - 1;
+        let shape = batch.input_ids.len();
+
+        // Create Cuda tensors
+        let input_ids = Tensor::from_vec(batch.input_ids, shape, &self.device)?;
+        let position_ids = Tensor::from_vec(batch.position_ids, shape, &self.device)?;
+        let cu_seqlens = Tensor::from_vec(
+            batch.cumulative_seq_lengths.clone(),
+            batch_size + 1,
+            &self.device,
+        )?;
+
+        let mut hidden_states = self.embeddings.forward(&input_ids)?;
+
+        let cos = self.cos_cache.index_select(&position_ids, 0)?;
+        let sin = self.sin_cache.index_select(&position_ids, 0)?;
+
+        let mut residual = None;
+        for layer in &self.layers {
+            let (h, r) = layer.forward(
+                &hidden_states,
+                residual.as_ref(),
+                &cu_seqlens,
+                &cos,
+                &sin,
+                batch.max_length as usize,
+            )?;
+            hidden_states = h;
+            residual = Some(r);
+        }
+
+        let (outputs, _) = self.norm.forward(&hidden_states, residual.as_ref())?;
+
+        let has_pooling_requests = !batch.pooled_indices.is_empty();
+        let has_raw_requests = !batch.raw_indices.is_empty();
+
+        let pooled_embeddings = if has_pooling_requests {
+            match self.pool {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
+                    if batch_size > 1 {
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
+
+                        // If raw_indices is empty, we don't need to do anything with
+                        // the pooled_indices
+                        if has_raw_requests {
+                            // We need the pooled indices to select the correct cls indices
+                            let pooled_indices = Tensor::from_vec(
+                                batch.pooled_indices.clone(),
+                                batch.pooled_indices.len(),
+                                &self.device,
+                            )?;
+
+                            // Only select indices that requires pooling
+                            indices = indices.index_select(&pooled_indices, 0)?
+                        }
+
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
+                    } else {
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
+                    }
+                }
+                // Mean pooling
+                Pool::Mean => {
+                    if batch_size > 1 {
+                        // for each request that requires pooling
+                        let results: Result<Vec<Tensor>> = batch
+                            .pooled_indices
+                            .into_iter()
+                            .map(|i| {
+                                let i = i as usize;
+                                let start = batch.cumulative_seq_lengths[i];
+                                let len = batch.cumulative_seq_lengths[i + 1] - start;
+
+                                // Mean
+                                let embeddings = outputs.narrow(0, start as usize, len as usize)?;
+                                embeddings.sum_keepdim(0)? / (len as f64)
+                            })
+                            .collect();
+
+                        // Concatenate all results
+                        Some(Tensor::cat(&results?, 0)?)
+                    } else {
+                        Some((outputs.sum_keepdim(0)? / (batch.max_length as f64))?)
+                    }
+                }
+                Pool::Splade => {
+                    unreachable!();
+                }
+            }
+        } else {
+            None
+        };
+
+        let raw_embeddings = if has_raw_requests {
+            if batch_size > 1 && has_pooling_requests {
+                // Create indexing vector for the embeddings
+                let mut final_indices: Vec<u32> = Vec::with_capacity(shape);
+                for i in batch.raw_indices.into_iter() {
+                    let i = i as usize;
+                    // Get start/end token index of this specific member of the batch
+                    let start = batch.cumulative_seq_lengths[i];
+                    let end = batch.cumulative_seq_lengths[i + 1];
+
+                    for j in start..end {
+                        // Add indices for the tokens of this specific member of the batch
+                        final_indices.push(j);
+                    }
+                }
+
+                let final_indices_length = final_indices.len();
+                let final_indices =
+                    Tensor::from_vec(final_indices, final_indices_length, &self.device)?;
+
+                // Select the tokens with final indices
+                Some(outputs.index_select(&final_indices, 0)?)
+            } else {
+                Some(outputs)
+            }
+        } else {
+            None
+        };
+
+        Ok((pooled_embeddings, raw_embeddings))
+    }
+}
+
+impl Model for FlashQwen2Model {
+    fn is_padded(&self) -> bool {
+        false
+    }
+    fn embed(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        self.forward(batch)
+    }
+}
diff --git a/backends/candle/src/models/gte.rs b/backends/candle/src/models/gte.rs
new file mode 100644
index 00000000..e5e75638
--- /dev/null
+++ b/backends/candle/src/models/gte.rs
@@ -0,0 +1,35 @@
+use crate::layers::HiddenAct;
+use crate::models::PositionEmbeddingType;
+use serde::Deserialize;
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct NTKScaling {
+    pub factor: f32,
+}
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+#[serde(tag = "type", rename_all = "kebab-case")]
+pub enum RopeScaling {
+    Ntk(NTKScaling),
+}
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct GTEConfig {
+    pub vocab_size: usize,
+    pub hidden_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub intermediate_size: usize,
+    pub hidden_act: HiddenAct,
+    pub max_position_embeddings: usize,
+    pub type_vocab_size: usize,
+    pub layer_norm_type: String,
+    pub layer_norm_eps: f32,
+    pub position_embedding_type: PositionEmbeddingType,
+    pub rope_theta: f32,
+    pub rope_scaling: Option<RopeScaling>,
+    #[serde(default)]
+    pub logn_attention_scale: bool,
+    #[serde(default)]
+    pub logn_attention_clip1: bool,
+}
diff --git a/backends/candle/src/models/jina.rs b/backends/candle/src/models/jina.rs
index 768e1a6f..ecee8bfe 100644
--- a/backends/candle/src/models/jina.rs
+++ b/backends/candle/src/models/jina.rs
@@ -363,6 +363,7 @@ impl JinaBertModel {
                 vb.dtype(),
             )?),
             PositionEmbeddingType::Absolute => None,
+            _ => candle::bail!("not supported"),
         };
 
         let pool = match model_type {
@@ -373,6 +374,9 @@ impl JinaBertModel {
                 if pool == Pool::Splade {
                     candle::bail!("`splade` is not supported for Jina")
                 }
+                if pool == Pool::LastToken {
+                    candle::bail!("`last_token` is not supported for Jina");
+                }
                 pool
             }
         };
@@ -594,6 +598,8 @@ impl JinaBertModel {
             let pooled_embeddings = match self.pool {
                 // CLS pooling
                 Pool::Cls => outputs.i((.., 0))?,
+                // Last token pooling is not supported for this model
+                Pool::LastToken => unreachable!(),
                 // Mean pooling
                 Pool::Mean => {
                     if let Some(ref attention_mask) = attention_mask {
diff --git a/backends/candle/src/models/jina_code.rs b/backends/candle/src/models/jina_code.rs
index 348f5892..5f13fe08 100644
--- a/backends/candle/src/models/jina_code.rs
+++ b/backends/candle/src/models/jina_code.rs
@@ -352,15 +352,19 @@ impl JinaCodeBertModel {
                 vb.dtype(),
             )?),
             PositionEmbeddingType::Absolute => None,
+            _ => candle::bail!("not supported"),
         };
 
         let pool = match model_type {
             ModelType::Classifier => {
-                candle::bail!("`classifier` model type is not supported for Jina")
+                candle::bail!("`classifier` model type is not supported for JinaCode")
             }
             ModelType::Embedding(pool) => {
                 if pool == Pool::Splade {
-                    candle::bail!("`splade` is not supported for Jina")
+                    candle::bail!("`splade` is not supported for JinaCode")
+                }
+                if pool == Pool::LastToken {
+                    candle::bail!("`last_token` is not supported for JinaCode");
                 }
                 pool
             }
@@ -583,6 +587,8 @@ impl JinaCodeBertModel {
             let pooled_embeddings = match self.pool {
                 // CLS pooling
                 Pool::Cls => outputs.i((.., 0))?,
+                // Last token pooling is not supported for this model
+                Pool::LastToken => unreachable!(),
                 // Mean pooling
                 Pool::Mean => {
                     if let Some(ref attention_mask) = attention_mask {
diff --git a/backends/candle/src/models/mistral.rs b/backends/candle/src/models/mistral.rs
new file mode 100644
index 00000000..33c5ab00
--- /dev/null
+++ b/backends/candle/src/models/mistral.rs
@@ -0,0 +1,19 @@
+use crate::layers::HiddenAct;
+use serde::Deserialize;
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct MistralConfig {
+    pub vocab_size: usize,
+    pub hidden_size: usize,
+    pub intermediate_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub num_key_value_heads: usize,
+    pub hidden_act: HiddenAct,
+    pub max_position_embeddings: usize,
+    pub initializer_range: f64,
+    pub rms_norm_eps: f32,
+    pub model_type: Option<String>,
+    pub rope_theta: f32,
+    pub sliding_window: Option<usize>,
+}
diff --git a/backends/candle/src/models/mod.rs b/backends/candle/src/models/mod.rs
index a7d6b267..b1e9f937 100644
--- a/backends/candle/src/models/mod.rs
+++ b/backends/candle/src/models/mod.rs
@@ -8,6 +8,7 @@ mod bert;
 mod distilbert;
 mod jina;
 mod jina_code;
+mod mistral;
 mod nomic;
 
 #[cfg(feature = "cuda")]
@@ -25,12 +26,26 @@ mod flash_nomic;
 #[cfg(feature = "cuda")]
 mod flash_distilbert;
 
+#[cfg(feature = "cuda")]
+mod flash_gte;
+#[cfg(feature = "cuda")]
+mod flash_mistral;
+
+#[cfg(feature = "cuda")]
+mod flash_qwen2;
+mod gte;
+mod qwen2;
+
 pub use bert::{BertConfig, BertModel, PositionEmbeddingType};
 use candle::{Result, Tensor};
 pub use distilbert::{DistilBertConfig, DistilBertModel};
+#[allow(unused_imports)]
+pub use gte::{GTEConfig, NTKScaling, RopeScaling};
 pub use jina::JinaBertModel;
 pub use jina_code::JinaCodeBertModel;
+pub use mistral::MistralConfig;
 pub use nomic::{NomicBertModel, NomicConfig};
+pub use qwen2::Qwen2Config;
 use text_embeddings_backend_core::Batch;
 
 #[cfg(feature = "cuda")]
@@ -48,6 +63,15 @@ pub use flash_nomic::FlashNomicBertModel;
 #[cfg(feature = "cuda")]
 pub use flash_distilbert::FlashDistilBertModel;
 
+#[cfg(feature = "cuda")]
+pub use flash_mistral::FlashMistralModel;
+
+#[cfg(feature = "cuda")]
+pub use flash_gte::FlashGTEModel;
+
+#[cfg(feature = "cuda")]
+pub use flash_qwen2::FlashQwen2Model;
+
 pub(crate) trait Model {
     fn is_padded(&self) -> bool;
 
diff --git a/backends/candle/src/models/nomic.rs b/backends/candle/src/models/nomic.rs
index 4f9e7551..cdaaea92 100644
--- a/backends/candle/src/models/nomic.rs
+++ b/backends/candle/src/models/nomic.rs
@@ -405,6 +405,9 @@ impl NomicBertModel {
                 if pool == Pool::Splade {
                     candle::bail!("`splade` is not supported for Nomic")
                 }
+                if pool == Pool::LastToken {
+                    candle::bail!("`last_token` is not supported for Nomic");
+                }
                 pool
             }
         };
@@ -610,6 +613,8 @@ impl NomicBertModel {
             let pooled_embeddings = match self.pool {
                 // CLS pooling
                 Pool::Cls => outputs.i((.., 0))?,
+                // Last token pooling is not supported for this model
+                Pool::LastToken => unreachable!(),
                 // Mean pooling
                 Pool::Mean => {
                     if let Some(ref attention_mask) = attention_mask {
diff --git a/backends/candle/src/models/qwen2.rs b/backends/candle/src/models/qwen2.rs
new file mode 100644
index 00000000..8d2d1f86
--- /dev/null
+++ b/backends/candle/src/models/qwen2.rs
@@ -0,0 +1,18 @@
+use crate::layers::HiddenAct;
+use serde::Deserialize;
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct Qwen2Config {
+    pub vocab_size: usize,
+    pub hidden_size: usize,
+    pub intermediate_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub num_key_value_heads: usize,
+    pub hidden_act: HiddenAct,
+    pub max_position_embeddings: usize,
+    pub rms_norm_eps: f32,
+    pub rope_theta: f32,
+    pub sliding_window: usize,
+    pub use_sliding_window: bool,
+}
diff --git a/backends/candle/tests/common.rs b/backends/candle/tests/common.rs
index d7ebc67d..0c069a47 100644
--- a/backends/candle/tests/common.rs
+++ b/backends/candle/tests/common.rs
@@ -1,5 +1,5 @@
 use anyhow::Result;
-use hf_hub::api::sync::ApiBuilder;
+use hf_hub::api::sync::{ApiBuilder, ApiError, ApiRepo};
 use hf_hub::{Repo, RepoType};
 use insta::internals::YamlMatcher;
 use serde::{Deserialize, Serialize};
@@ -25,7 +25,7 @@ impl Score {
 impl PartialEq for Score {
     fn eq(&self, other: &Self) -> bool {
         // Default tolerance for equality
-        self.is_close(other, 6e-3)
+        self.is_close(other, 5e-3)
     }
 }
 
@@ -51,6 +51,44 @@ impl From<Vec<Vec<f32>>> for SnapshotScores {
     }
 }
 
+#[derive(Serialize, Deserialize, Debug)]
+pub struct SnapEmbedding(Vec<f32>);
+
+impl PartialEq for SnapEmbedding {
+    fn eq(&self, other: &Self) -> bool {
+        assert_eq!(self.0.len(), other.0.len());
+
+        let mut sumxx = 0.0;
+        let mut sumyy = 0.0;
+        let mut sumxy = 0.0;
+
+        for (x, y) in self.0.iter().zip(other.0.iter()) {
+            sumxx += x * x;
+            sumyy += y * y;
+            sumxy += x * y;
+        }
+
+        (sumxy / (sumxx * sumyy).sqrt()) > 0.999
+    }
+}
+
+#[derive(Serialize, Deserialize, Debug, PartialEq)]
+pub struct SnapshotEmbeddings(Vec<SnapEmbedding>);
+
+impl Deref for SnapshotEmbeddings {
+    type Target = Vec<SnapEmbedding>;
+
+    fn deref(&self) -> &Self::Target {
+        &self.0
+    }
+}
+
+impl From<Vec<Vec<f32>>> for SnapshotEmbeddings {
+    fn from(value: Vec<Vec<f32>>) -> Self {
+        Self(value.into_iter().map(|v| SnapEmbedding(v)).collect())
+    }
+}
+
 pub fn sort_embeddings(embeddings: Embeddings) -> (Vec<Vec<f32>>, Vec<Vec<f32>>) {
     let mut pooled_embeddings = Vec::new();
     let mut raw_embeddings = Vec::new();
@@ -69,7 +107,11 @@ pub fn download_artifacts(
     model_id: &'static str,
     revision: Option<&'static str>,
 ) -> Result<PathBuf> {
-    let builder = ApiBuilder::new().with_progress(false);
+    let mut builder = ApiBuilder::new().with_progress(false);
+
+    if let Some(cache_dir) = std::env::var_os("HUGGINGFACE_HUB_CACHE") {
+        builder = builder.with_cache_dir(cache_dir.into());
+    }
 
     let api = builder.build().unwrap();
     let api_repo = if let Some(revision) = revision {
@@ -85,23 +127,66 @@ pub fn download_artifacts(
     api_repo.get("config.json")?;
     api_repo.get("tokenizer.json")?;
 
-    let model_root = match api_repo.get("model.safetensors") {
+    let model_files = match download_safetensors(&api_repo) {
         Ok(p) => p,
         Err(_) => {
+            tracing::warn!("safetensors weights not found. Using `pytorch_model.bin` instead. Model loading will be significantly slower.");
+            tracing::info!("Downloading `pytorch_model.bin`");
             let p = api_repo.get("pytorch_model.bin")?;
-            tracing::warn!("`model.safetensors` not found. Using `pytorch_model.bin` instead. Model loading will be significantly slower.");
-            p
+            vec![p]
         }
-    }
-        .parent().unwrap()
-        .to_path_buf();
+    };
+    let model_root = model_files[0].parent().unwrap().to_path_buf();
     Ok(model_root)
 }
 
+fn download_safetensors(api: &ApiRepo) -> Result<Vec<PathBuf>, ApiError> {
+    // Single file
+    tracing::info!("Downloading `model.safetensors`");
+    match api.get("model.safetensors") {
+        Ok(p) => return Ok(vec![p]),
+        Err(err) => tracing::warn!("Could not download `model.safetensors`: {}", err),
+    };
+
+    // Sharded weights
+    // Download and parse index file
+    tracing::info!("Downloading `model.safetensors.index.json`");
+    let index_file = api.get("model.safetensors.index.json")?;
+    let index_file_string: String =
+        std::fs::read_to_string(index_file).expect("model.safetensors.index.json is corrupted");
+    let json: serde_json::Value = serde_json::from_str(&index_file_string)
+        .expect("model.safetensors.index.json is corrupted");
+
+    let weight_map = match json.get("weight_map") {
+        Some(serde_json::Value::Object(map)) => map,
+        _ => panic!("model.safetensors.index.json is corrupted"),
+    };
+
+    let mut safetensors_filenames = std::collections::HashSet::new();
+    for value in weight_map.values() {
+        if let Some(file) = value.as_str() {
+            safetensors_filenames.insert(file.to_string());
+        }
+    }
+
+    // Download weight files
+    let mut safetensors_files = Vec::new();
+    for n in safetensors_filenames {
+        tracing::info!("Downloading `{}`", n);
+        safetensors_files.push(api.get(&n)?);
+    }
+
+    Ok(safetensors_files)
+}
+
 pub fn relative_matcher() -> YamlMatcher<SnapshotScores> {
     YamlMatcher::new()
 }
 
+pub fn cosine_matcher() -> YamlMatcher<SnapshotEmbeddings> {
+    YamlMatcher::new()
+}
+
 pub fn load_tokenizer(model_root: &Path) -> Result<Tokenizer> {
     // Load tokenizer
     let tokenizer_path = model_root.join("tokenizer.json");
diff --git a/backends/candle/tests/snapshots/test_flash_gte__gte_batch.snap b/backends/candle/tests/snapshots/test_flash_gte__gte_batch.snap
new file mode 100644
index 00000000..d8bbd66e
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_gte__gte_batch.snap
@@ -0,0 +1,2309 @@
+---
+source: backends/candle/tests/test_flash_gte.rs
+assertion_line: 37
+expression: embeddings_batch
+---
+- - 0.4428711
+  - 0.6118164
+  - 0.07043457
+  - -0.5917969
+  - 0.88183594
+  - -0.10839844
+  - 0.0881958
+  - -0.20483398
+  - 1.9345703
+  - 0.6230469
+  - 0.59521484
+  - 0.18737793
+  - 0.030548096
+  - -0.078308105
+  - 0.79589844
+  - -0.23474121
+  - -0.8720703
+  - 1.6923828
+  - 0.12597656
+  - -1.9267578
+  - -0.34472656
+  - -1.9326172
+  - 0.89990234
+  - -0.2006836
+  - 0.024230957
+  - 0.2878418
+  - 0.3642578
+  - 0.17956543
+  - -0.51123047
+  - 0.025817871
+  - -0.8964844
+  - 0.14147949
+  - -0.39257813
+  - -0.8486328
+  - 0.005218506
+  - 0.42358398
+  - 0.23181152
+  - 1.1630859
+  - -1.1455078
+  - 0.3388672
+  - -2.1484375
+  - -1.0146484
+  - -0.2841797
+  - 0.021499634
+  - 0.16235352
+  - 0.39453125
+  - -0.9511719
+  - 0.41064453
+  - -0.15563965
+  - -0.484375
+  - -0.07080078
+  - 0.07312012
+  - 0.56103516
+  - 0.028213501
+  - -0.027938843
+  - 0.43896484
+  - 1.0136719
+  - 0.7788086
+  - -0.5644531
+  - -1.9824219
+  - -0.011169434
+  - 0.50634766
+  - -0.39331055
+  - -0.3334961
+  - -1.1679688
+  - -0.07208252
+  - 0.008544922
+  - 0.7788086
+  - -0.10021973
+  - -0.30859375
+  - 0.35888672
+  - -0.44848633
+  - -0.08679199
+  - -1.0791016
+  - 1.3193359
+  - -1.0273438
+  - 0.34228516
+  - -0.45385742
+  - 0.8027344
+  - 0.50634766
+  - 0.703125
+  - -0.96533203
+  - -0.042938232
+  - -1.0927734
+  - 0.35205078
+  - -0.56689453
+  - -0.5341797
+  - 0.23571777
+  - -2.0234375
+  - -0.26342773
+  - 0.1998291
+  - 0.7739258
+  - 0.17797852
+  - 1.2255859
+  - 0.9814453
+  - -0.2902832
+  - 1.2080078
+  - 0.50097656
+  - 0.2479248
+  - -0.2166748
+  - 0.29052734
+  - -0.5004883
+  - 0.26513672
+  - 0.5024414
+  - -0.042266846
+  - -0.013465881
+  - -0.6376953
+  - 0.64501953
+  - -1.7958984
+  - 0.25512695
+  - -0.20458984
+  - -0.12927246
+  - 0.19665527
+  - 0.8076172
+  - -0.96777344
+  - 0.26391602
+  - -0.5649414
+  - -0.58740234
+  - -0.15466309
+  - 0.7392578
+  - 0.5336914
+  - 0.60253906
+  - 1.1875
+  - -0.37719727
+  - 0.8198242
+  - -0.2401123
+  - 0.40527344
+  - 0.17114258
+  - -0.32128906
+  - -0.75439453
+  - 0.056488037
+  - 0.16491699
+  - 0.6899414
+  - -0.6171875
+  - -0.05316162
+  - 0.43017578
+  - -0.6074219
+  - 0.040374756
+  - -0.8618164
+  - -0.7192383
+  - 0.7402344
+  - -0.42138672
+  - 0.26831055
+  - -1.5429688
+  - 0.1706543
+  - 1.2158203
+  - -0.17077637
+  - -0.13964844
+  - -0.5449219
+  - -0.59033203
+  - -0.7783203
+  - -0.18786621
+  - -0.34838867
+  - 0.6230469
+  - -0.5878906
+  - -0.90234375
+  - -0.7324219
+  - -0.81933594
+  - 0.4819336
+  - -1.1591797
+  - 1.2109375
+  - -1.0517578
+  - 0.25219727
+  - 1.2841797
+  - 0.06347656
+  - -0.72021484
+  - -0.3671875
+  - 0.07330322
+  - -0.022644043
+  - -0.3713379
+  - -0.008430481
+  - 0.3564453
+  - -0.8354492
+  - -0.59521484
+  - -0.40405273
+  - -0.027923584
+  - -0.3100586
+  - -1.109375
+  - -0.030593872
+  - 0.14086914
+  - -1.2128906
+  - 0.03488159
+  - 0.35424805
+  - -1.5683594
+  - 0.17773438
+  - -0.66796875
+  - -1.9804688
+  - -0.35327148
+  - -0.2927246
+  - -1.2011719
+  - -0.4165039
+  - 0.36645508
+  - -0.049957275
+  - -0.87353516
+  - -0.9086914
+  - 0.4790039
+  - -1.4599609
+  - 0.7866211
+  - -0.27490234
+  - 0.08477783
+  - 0.2220459
+  - 0.0017213821
+  - -1.0927734
+  - -0.006439209
+  - -0.62060547
+  - -0.68115234
+  - 0.4572754
+  - -0.61572266
+  - -0.7871094
+  - -1.3847656
+  - 1.5566406
+  - 0.2680664
+  - 0.8466797
+  - -0.2578125
+  - -0.76416016
+  - 0.6484375
+  - -0.05279541
+  - -0.1307373
+  - -0.11853027
+  - -0.6357422
+  - -0.17700195
+  - 0.9472656
+  - -0.4958496
+  - 0.015464783
+  - -0.23168945
+  - -1.6748047
+  - -0.8911133
+  - 0.86865234
+  - 1.0722656
+  - 0.34228516
+  - 0.7182617
+  - -0.5488281
+  - -0.45166016
+  - -0.3022461
+  - 0.45336914
+  - -0.28808594
+  - -0.7626953
+  - 0.016067505
+  - 0.63183594
+  - 0.46923828
+  - -0.07373047
+  - 1.7832031
+  - -0.41796875
+  - -0.89453125
+  - 0.07757568
+  - -0.29589844
+  - -1.0673828
+  - 1.3320313
+  - 1.4160156
+  - 0.20703125
+  - -0.1060791
+  - 0.1508789
+  - 0.37231445
+  - 0.05053711
+  - -0.68896484
+  - 0.7397461
+  - 0.15771484
+  - 0.5595703
+  - -0.8198242
+  - -0.17712402
+  - -0.2479248
+  - -0.066345215
+  - 0.4194336
+  - 1.0517578
+  - -1.6650391
+  - 0.90283203
+  - 0.22937012
+  - -0.5908203
+  - -0.79785156
+  - -0.003993988
+  - -0.47314453
+  - 0.19116211
+  - -0.39038086
+  - 0.6333008
+  - -0.28686523
+  - -0.8720703
+  - 0.080200195
+  - -1.2587891
+  - 0.22790527
+  - 0.55126953
+  - -0.47485352
+  - -0.20471191
+  - 0.75390625
+  - -1.0488281
+  - 0.9902344
+  - 1.2275391
+  - 0.40966797
+  - -0.36791992
+  - 0.54833984
+  - -0.32788086
+  - 0.2208252
+  - 1.1289063
+  - -0.017410278
+  - -0.67089844
+  - 0.7236328
+  - 0.20788574
+  - 0.55566406
+  - 0.7104492
+  - -0.16589355
+  - 0.57958984
+  - 1.1494141
+  - -0.15930176
+  - -0.4963379
+  - 0.016326904
+  - 1.5634766
+  - -0.5029297
+  - 1.2011719
+  - -1.0029297
+  - -0.3557129
+  - -0.31079102
+  - -0.12670898
+  - 0.81933594
+  - 0.75683594
+  - 1.1630859
+  - 1.6669922
+  - -0.8022461
+  - -0.3034668
+  - 0.3798828
+  - -0.71484375
+  - -0.7470703
+  - 1.1904297
+  - -0.95654297
+  - 0.30297852
+  - 0.16992188
+  - 0.8173828
+  - -0.8798828
+  - 1.1376953
+  - -0.4543457
+  - 0.6142578
+  - -0.47680664
+  - 1.1181641
+  - 0.01398468
+  - -0.49560547
+  - -0.039031982
+  - 0.2878418
+  - -0.71533203
+  - 0.82128906
+  - -0.03768921
+  - 0.60546875
+  - 0.7578125
+  - -0.3322754
+  - 0.86376953
+  - -0.71435547
+  - 0.7597656
+  - -1.3496094
+  - 0.18652344
+  - -0.39990234
+  - 0.31103516
+  - 0.16809082
+  - 1.2626953
+  - -0.42919922
+  - 0.2434082
+  - 0.83935547
+  - -1.3994141
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diff --git a/backends/candle/tests/snapshots/test_flash_gte__gte_single.snap b/backends/candle/tests/snapshots/test_flash_gte__gte_single.snap
new file mode 100644
index 00000000..96c169b0
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_gte__gte_single.snap
@@ -0,0 +1,773 @@
+---
+source: backends/candle/tests/test_flash_gte.rs
+assertion_line: 48
+expression: embeddings_single
+---
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diff --git a/backends/candle/tests/snapshots/test_flash_mistral__mistral_batch.snap b/backends/candle/tests/snapshots/test_flash_mistral__mistral_batch.snap
new file mode 100644
index 00000000..acea099b
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_mistral__mistral_batch.snap
@@ -0,0 +1,12293 @@
+---
+source: backends/candle/tests/test_flash_mistral.rs
+assertion_line: 37
+expression: embeddings_batch
+---
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diff --git a/backends/candle/tests/snapshots/test_flash_mistral__mistral_single.snap b/backends/candle/tests/snapshots/test_flash_mistral__mistral_single.snap
new file mode 100644
index 00000000..b238aa6a
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_mistral__mistral_single.snap
@@ -0,0 +1,4101 @@
+---
+source: backends/candle/tests/test_flash_mistral.rs
+assertion_line: 48
+expression: embeddings_single
+---
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diff --git a/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_batch.snap b/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_batch.snap
new file mode 100644
index 00000000..7a252f73
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_batch.snap
@@ -0,0 +1,4613 @@
+---
+source: backends/candle/tests/test_flash_qwen2.rs
+assertion_line: 61
+expression: embeddings_batch
+---
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diff --git a/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_single.snap b/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_single.snap
new file mode 100644
index 00000000..362e1ebf
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_single.snap
@@ -0,0 +1,1541 @@
+---
+source: backends/candle/tests/test_flash_qwen2.rs
+assertion_line: 72
+expression: embeddings_single
+---
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+  - -7.125
+  - 0.2919922
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+  - 0.421875
+  - -1.8300781
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+  - 2.0429688
+  - -3.1308594
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+  - -2.5898438
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+  - -4.4101563
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+  - 0.2614746
+  - -5.1835938
+  - -4.4726563
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+  - 2.2910156
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+  - -5.421875
+  - 15.734375
+  - 4.890625
+  - 2.6542969
+  - 0.8989258
+  - -7.96875
+  - -11.1640625
+  - 4.6835938
+  - 0.24902344
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+  - -4.6757813
+  - 8.921875
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+  - 0.6982422
+  - 2.0800781
+  - -4.9375
+  - 0.091918945
+  - 1.5839844
+  - 0.8911133
+  - -0.09967041
+  - -4.3867188
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+  - 0.36157227
+  - 3.2910156
+  - 0.29663086
+  - -1.2138672
+  - 3.0644531
+  - -2.1875
+  - -8.7578125
+  - 6.421875
+  - 0.1496582
+  - -3.0273438
+  - -3.7128906
+  - 4.4882813
+  - -3.3886719
+  - -4.0507813
+  - 0.31323242
+  - 1.7900391
+  - 5.8515625
+  - 4.21875
+  - -0.57714844
+  - -1.3984375
+  - 1.4736328
+  - -1.1748047
+  - 2.3183594
+  - -3.3847656
+  - -5.015625
+  - -4.84375
+  - -0.9609375
+  - -1.7646484
+  - -0.703125
+  - 0.055725098
+  - -2.2929688
+  - -0.08001709
+  - -3.7246094
+  - 7.8984375
+  - 4.2265625
+  - -2.875
+  - -3.3457031
+  - 5.640625
+  - -2.6347656
+  - 2.0097656
+  - -0.5444336
+  - 1.3837891
+  - -0.0869751
+  - -3.8085938
+  - -3.0058594
+  - 4.2109375
+  - 4.7578125
+  - -4.3320313
+  - 11.1640625
+  - -1.6669922
+  - -0.8486328
+  - -5.8945313
+  - -5.8632813
+  - -0.9379883
+  - -8.4453125
+  - -5.4570313
+  - 1.1132813
+  - -2.1230469
+  - -7.9101563
+  - 5.0976563
+  - -5.2382813
+  - -0.027252197
+  - 2.1542969
+  - 3.9746094
+  - 6.4414063
+  - 4.1953125
+  - 5.4414063
+  - -2.6855469
+  - 0.093933105
+  - -2.6210938
+  - 5.515625
+  - -1.9550781
+  - 3.7851563
+  - -2.2285156
+  - 1.6621094
+  - 2.8261719
+  - -0.21313477
+  - 5.1914063
+  - 7.0585938
+  - -2.84375
+  - 6.21875
+  - -3.6640625
+  - 5.7851563
+  - -0.29492188
+  - -3.5859375
+  - -1.5400391
+  - 6.1171875
+  - -0.92285156
+  - 2.9316406
+  - -8.7265625
+  - -0.25317383
+  - 0.546875
+  - 2.9980469
+  - -2.3164063
+  - 1.6005859
+  - 7.4179688
+  - -6.0273438
+  - -0.48217773
+  - 0.13757324
+  - 0.84765625
+  - -6.4023438
+  - -1.2714844
+  - -2.8984375
+  - -1.1894531
+  - 2.5839844
+  - -3.8261719
+  - 3.5957031
+  - 11.265625
+  - -9.9765625
+  - 3.890625
+  - 7.6054688
+  - -2.4746094
+  - 1.7714844
+  - -0.30078125
+  - -4.1523438
+  - 0.3125
+  - -4.4492188
+  - -0.3779297
+  - 3.453125
+  - -1.7099609
+  - 1.8554688
+  - -8.171875
+  - 3.0625
+  - 6.2382813
+  - 0.23913574
+  - 8.5234375
+  - -3.0488281
+  - -1.9902344
+  - 4.5351563
+  - 3.2519531
+  - 0.7084961
+  - -2.8632813
+  - 1.2373047
+  - -6.3320313
+  - 1.2314453
+  - 1.2734375
+  - -2.9257813
+  - 1.3251953
+  - -2.2890625
+  - 0.7158203
+  - -6.4804688
+  - -4.09375
+  - -2.4257813
+  - -0.36157227
+  - 1.6533203
+  - -6.1914063
+  - 4.1601563
+  - -13.125
+  - -1.1171875
+  - 6.5039063
+  - 3.3125
+  - 4.0625
+  - 2.9902344
+  - 2.9628906
+  - 7.8007813
+  - -2.71875
+  - -3.8261719
+  - 6.375
+  - 3.1621094
+  - -8.0859375
+  - -1.3447266
+  - -4.8710938
+  - -2.1953125
+  - -0.62402344
+  - -1.9199219
+  - 4.578125
+  - -3.9277344
+  - -3.0625
+  - -1.4238281
+  - 20.28125
+  - -1.7685547
+  - 2.109375
+  - -2.984375
+  - 0.41845703
+  - -4.765625
+  - -0.69091797
+  - 1.3945313
+  - 2.0566406
+  - 3.9042969
+  - 1.7929688
+  - 0.6171875
+  - -2.4375
+  - -0.94677734
+  - 5.6601563
+  - 0.81689453
+  - 0.041137695
+  - -1.7695313
+  - 6.6289063
+  - 0.49926758
+  - -6.5
+  - -3.3007813
+  - -12.1796875
+  - -0.38208008
+  - -0.8300781
+  - -1.9794922
+  - 5.0234375
+  - -3.1015625
+  - 0.17346191
+  - 10.1328125
+  - 4.4179688
+  - -3.2011719
+  - 1.7480469
+  - -8.9765625
+  - 1.4941406
+  - 6.7226563
+  - 9.0390625
+  - -2.6210938
+  - -5.4492188
+  - 4.4453125
+  - -0.6225586
+  - -5.9101563
+  - 0.28149414
+  - 4.0273438
+  - 7.40625
+  - 4.3203125
+  - -6.875
+  - -2.65625
+  - -4.1875
+  - -1.9970703
+  - 1.03125
+  - -9.4453125
+  - -6.1835938
+  - -4.7851563
+  - -10.5
+  - -0.062408447
+  - 1.7246094
+  - -5.8710938
+  - 6.8046875
+  - -2.2578125
+  - -2.8085938
+  - 5.453125
+  - -2.2617188
+  - 7.625
+  - 0.67285156
+  - 0.17578125
+  - -12.84375
+  - -0.18786621
+  - -1.0966797
+  - 4.1523438
+  - -2.2402344
+  - 3.0917969
+  - 3.5488281
+  - -7.2695313
+  - 0.022140503
+  - 1.2539063
+  - -9.3203125
+  - -8.265625
+  - -2.2792969
+  - -0.7631836
+  - 6.8046875
+  - -1.9013672
+  - -11.8046875
+  - -0.24047852
+  - 3.5859375
+  - -2.7890625
+  - 0.5673828
+  - -5.1953125
+  - -2.3183594
+  - -3.0273438
+  - -4.1875
+  - 3.7460938
+  - -2.4316406
+  - -2.0429688
+  - -3.3789063
+  - -2.3417969
+  - 3.6582031
+  - 9.1484375
+  - -5.2226563
+  - 3.0546875
+  - 0.36157227
+  - -1.1728516
+  - -5.2539063
+  - 2.1308594
+  - -10.15625
+  - 1.9169922
+  - 0.2722168
+  - 0.60839844
+  - 3.9472656
+  - 5.1679688
+  - 0.6791992
+  - -10.6953125
+  - -2.1757813
+  - 1.3056641
+  - -1.2558594
+  - 2.8378906
+  - -1.3095703
+  - -0.5136719
+  - 1.3476563
+  - 12.265625
+  - -1.0419922
+  - 4.5078125
+  - 7.2890625
+  - 1.2998047
+  - 1.9511719
+  - 3.9648438
+  - 1.5673828
+  - 1.5996094
+  - -2.0605469
+  - 1.8046875
+  - 4.3632813
+  - -7.7304688
+  - 2.1425781
+  - 0.7246094
+  - -4.0976563
+  - 0.8198242
+  - 12.375
+  - -7.5390625
+  - -10
+  - 1.4423828
+  - -0.7089844
+  - 0.7211914
+  - 9.21875
+  - -7.203125
+  - 1.078125
+  - 4.21875
+  - 2.0488281
+  - -4.5898438
+  - -6.4414063
+  - -4.6914063
+  - -3.4394531
+  - -1.21875
+  - 17.265625
+  - 2.96875
+  - 1.1923828
+  - 5.8125
+  - -4.9609375
+  - 2.5761719
+  - -2.640625
+  - -1.4892578
+  - -8.359375
+  - -0.8305664
+  - 3.8242188
+  - -2.7851563
+  - -0.5551758
+  - 2.0800781
+  - 10.5078125
+  - 6.3085938
+  - 4.9375
+  - 4.046875
+  - -6.2148438
+  - -1.1171875
+  - 3.1464844
+  - -5.7695313
+  - 6.1328125
+  - 2.7324219
+  - -0.15002441
+  - -6.1601563
+  - 0.4855957
+  - -5.0703125
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+  - 2.6796875
+  - 4.75
+  - 4.1015625
+  - -3.7539063
+  - 1.7714844
+  - 1.0996094
+  - -1.8242188
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+  - 1.6787109
+  - 7.546875
+  - 4.984375
+  - 2.171875
+  - -5.7109375
+  - -0.77441406
+  - 1.6904297
+  - -3.1386719
+  - -3.1113281
+  - -9.4765625
+  - -3.6054688
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+  - 1.6132813
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+  - 2.3945313
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+  - 1.5869141
+  - 0.16357422
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+  - -0.15612793
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+  - 5.5351563
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+  - -1.1142578
+  - 0.7270508
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+  - 4.140625
+  - -6.8867188
+  - 1.8105469
+  - 6.4296875
+  - -4.1367188
+  - 0.45458984
+  - 2.5019531
+  - 6.0742188
+  - -1.1699219
+  - 1.0537109
+  - 5.3554688
+  - 3.8125
+  - -2.2871094
+  - 0.9238281
+  - 4.9296875
+  - 1.9853516
+  - 9.484375
+  - 0.5942383
+  - -2.4199219
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+  - 2.2128906
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+  - -2.1054688
+  - 2.5566406
+  - -0.7841797
+  - -1.8798828
+  - -8.125
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+  - 0.09362793
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+  - -2.8378906
+  - 4.8515625
+  - 1.8964844
+  - -2.6269531
+  - 1.4208984
+  - 0.90185547
+  - 2.3457031
+  - -9.125
+  - 1.0859375
+  - 1.3027344
+  - -2.2382813
+  - 4.875
+  - -3.1582031
+  - -6.3789063
+  - -1.2128906
+  - 4.7773438
+  - 0.27392578
+  - -0.43188477
+  - 5.8125
+  - -4.1367188
+  - -4.4570313
+  - -7.8046875
+  - -3.3515625
diff --git a/backends/candle/tests/test_bert.rs b/backends/candle/tests/test_bert.rs
index 45d02577..1bd5017f 100644
--- a/backends/candle/tests/test_bert.rs
+++ b/backends/candle/tests/test_bert.rs
@@ -1,8 +1,8 @@
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings, SnapshotScores};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer, relative_matcher};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -28,10 +28,10 @@ fn test_mini() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -41,7 +41,7 @@ fn test_mini() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("mini_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
@@ -57,8 +57,8 @@ fn test_mini() -> Result<()> {
     );
 
     let (pooled_embeddings, raw_embeddings) = sort_embeddings(backend.embed(input_batch)?);
-    let pooled_embeddings = SnapshotScores::from(pooled_embeddings);
-    let raw_embeddings = SnapshotScores::from(raw_embeddings);
+    let pooled_embeddings = SnapshotEmbeddings::from(pooled_embeddings);
+    let raw_embeddings = SnapshotEmbeddings::from(raw_embeddings);
 
     assert_eq!(embeddings_batch[0], pooled_embeddings[0]);
     assert_eq!(raw_embeddings.len(), 8);
@@ -91,13 +91,13 @@ fn test_mini_pooled_raw() -> Result<()> {
         [1, 4, 5].to_vec(),
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, raw_embeddings) = sort_embeddings(backend.embed(input_batch)?);
-    let pooled_embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let pooled_embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_batch_pooled", pooled_embeddings_batch, &matcher);
 
-    let raw_embeddings_batch = SnapshotScores::from(raw_embeddings);
+    let raw_embeddings_batch = SnapshotEmbeddings::from(raw_embeddings);
     insta::assert_yaml_snapshot!("mini_batch_raw", raw_embeddings_batch, &matcher);
 
     // Check that the first token of each raw embeddings member is the same as the cls pooling ones
@@ -113,7 +113,7 @@ fn test_mini_pooled_raw() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_single_pooled", embeddings_single, &matcher);
 
     assert_eq!(pooled_embeddings_batch[0], embeddings_single[0]);
@@ -126,7 +126,7 @@ fn test_mini_pooled_raw() -> Result<()> {
     );
 
     let (_, raw_embeddings) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(raw_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(raw_embeddings);
     insta::assert_yaml_snapshot!("mini_single_raw", embeddings_single, &matcher);
 
     assert_eq!(raw_embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_flash_bert.rs b/backends/candle/tests/test_flash_bert.rs
index 1888a32b..ea150e7f 100644
--- a/backends/candle/tests/test_flash_bert.rs
+++ b/backends/candle/tests/test_flash_bert.rs
@@ -2,9 +2,9 @@
 
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings, SnapshotScores};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer, relative_matcher};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -34,10 +34,10 @@ fn test_flash_mini() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -47,7 +47,7 @@ fn test_flash_mini() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("mini_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
@@ -63,8 +63,8 @@ fn test_flash_mini() -> Result<()> {
     );
 
     let (pooled_embeddings, raw_embeddings) = sort_embeddings(backend.embed(input_batch)?);
-    let pooled_embeddings = SnapshotScores::from(pooled_embeddings);
-    let raw_embeddings = SnapshotScores::from(raw_embeddings);
+    let pooled_embeddings = SnapshotEmbeddings::from(pooled_embeddings);
+    let raw_embeddings = SnapshotEmbeddings::from(raw_embeddings);
 
     assert_eq!(embeddings_batch[0], pooled_embeddings[0]);
     assert_eq!(raw_embeddings.len(), 8);
@@ -101,13 +101,13 @@ fn test_flash_mini_pooled_raw() -> Result<()> {
         [1, 4, 5].to_vec(),
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, raw_embeddings) = sort_embeddings(backend.embed(input_batch)?);
-    let pooled_embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let pooled_embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_batch_pooled", pooled_embeddings_batch, &matcher);
 
-    let raw_embeddings_batch = SnapshotScores::from(raw_embeddings);
+    let raw_embeddings_batch = SnapshotEmbeddings::from(raw_embeddings);
     insta::assert_yaml_snapshot!("mini_batch_raw", raw_embeddings_batch, &matcher);
 
     // Check that the first token of each raw embeddings member is the same as the cls pooling ones
@@ -123,7 +123,7 @@ fn test_flash_mini_pooled_raw() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_single_pooled", embeddings_single, &matcher);
 
     assert_eq!(pooled_embeddings_batch[0], embeddings_single[0]);
@@ -136,7 +136,7 @@ fn test_flash_mini_pooled_raw() -> Result<()> {
     );
 
     let (_, raw_embeddings) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(raw_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(raw_embeddings);
     insta::assert_yaml_snapshot!("mini_single_raw", embeddings_single, &matcher);
 
     assert_eq!(raw_embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_flash_gte.rs b/backends/candle/tests/test_flash_gte.rs
new file mode 100644
index 00000000..20b06b2f
--- /dev/null
+++ b/backends/candle/tests/test_flash_gte.rs
@@ -0,0 +1,53 @@
+#![allow(dead_code, unused_imports)]
+mod common;
+
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
+use anyhow::Result;
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
+use text_embeddings_backend_candle::CandleBackend;
+use text_embeddings_backend_core::{Backend, ModelType, Pool};
+
+#[test]
+#[serial_test::serial]
+#[cfg(all(feature = "cuda", feature = "flash-attn"))]
+fn test_flash_gte() -> Result<()> {
+    let model_root = download_artifacts("Alibaba-NLP/gte-base-en-v1.5", None)?;
+    let tokenizer = load_tokenizer(&model_root)?;
+
+    let backend = CandleBackend::new(
+        model_root,
+        "float16".to_string(),
+        ModelType::Embedding(Pool::Cls),
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 1, 2].to_vec(),
+        vec![],
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("gte_batch", embeddings_batch, &matcher);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+
+    insta::assert_yaml_snapshot!("gte_single", embeddings_single, &matcher);
+    assert_eq!(embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(embeddings_batch[2], embeddings_single[0]);
+
+    Ok(())
+}
diff --git a/backends/candle/tests/test_flash_jina.rs b/backends/candle/tests/test_flash_jina.rs
index 4a5f8276..255b82a2 100644
--- a/backends/candle/tests/test_flash_jina.rs
+++ b/backends/candle/tests/test_flash_jina.rs
@@ -1,9 +1,9 @@
 #![allow(dead_code, unused_imports)]
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -30,10 +30,10 @@ fn test_flash_jina_small() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("jina_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -43,7 +43,7 @@ fn test_flash_jina_small() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("jina_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_flash_jina_code.rs b/backends/candle/tests/test_flash_jina_code.rs
index 508bf722..d84848dc 100644
--- a/backends/candle/tests/test_flash_jina_code.rs
+++ b/backends/candle/tests/test_flash_jina_code.rs
@@ -1,9 +1,9 @@
 #![allow(dead_code, unused_imports)]
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -30,10 +30,10 @@ fn test_flash_jina_code_base() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("jina_code_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -43,7 +43,7 @@ fn test_flash_jina_code_base() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("jina_code_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_flash_mistral.rs b/backends/candle/tests/test_flash_mistral.rs
new file mode 100644
index 00000000..71749c8b
--- /dev/null
+++ b/backends/candle/tests/test_flash_mistral.rs
@@ -0,0 +1,53 @@
+#![allow(dead_code, unused_imports)]
+mod common;
+
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
+use anyhow::Result;
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
+use text_embeddings_backend_candle::CandleBackend;
+use text_embeddings_backend_core::{Backend, ModelType, Pool};
+
+#[test]
+#[serial_test::serial]
+#[cfg(all(feature = "cuda", feature = "flash-attn"))]
+fn test_flash_mistral() -> Result<()> {
+    let model_root = download_artifacts("Salesforce/SFR-Embedding-2_R", None)?;
+    let tokenizer = load_tokenizer(&model_root)?;
+
+    let backend = CandleBackend::new(
+        model_root,
+        "float16".to_string(),
+        ModelType::Embedding(Pool::Mean),
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 1, 2].to_vec(),
+        vec![],
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("mistral_batch", embeddings_batch, &matcher);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+
+    insta::assert_yaml_snapshot!("mistral_single", embeddings_single, &matcher);
+    assert_eq!(embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(embeddings_batch[2], embeddings_single[0]);
+
+    Ok(())
+}
diff --git a/backends/candle/tests/test_flash_nomic.rs b/backends/candle/tests/test_flash_nomic.rs
index 3e9b6e1d..263bbe43 100644
--- a/backends/candle/tests/test_flash_nomic.rs
+++ b/backends/candle/tests/test_flash_nomic.rs
@@ -1,9 +1,9 @@
 #![allow(dead_code, unused_imports)]
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -30,10 +30,10 @@ fn test_flash_nomic_small() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("nomic_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -43,7 +43,7 @@ fn test_flash_nomic_small() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("nomic_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_flash_qwen2.rs b/backends/candle/tests/test_flash_qwen2.rs
new file mode 100644
index 00000000..38e45553
--- /dev/null
+++ b/backends/candle/tests/test_flash_qwen2.rs
@@ -0,0 +1,77 @@
+#![allow(dead_code, unused_imports)]
+
+mod common;
+
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
+use anyhow::Result;
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
+use text_embeddings_backend_candle::CandleBackend;
+use text_embeddings_backend_core::{Backend, ModelType, Pool};
+use tokenizers::processors::sequence::Sequence;
+use tokenizers::processors::template::TemplateProcessing;
+use tokenizers::{PostProcessorWrapper, Tokenizer};
+
+#[test]
+#[serial_test::serial]
+#[cfg(all(feature = "cuda", feature = "flash-attn"))]
+fn test_flash_qwen2() -> Result<()> {
+    let model_root = download_artifacts("Alibaba-NLP/gte-Qwen2-1.5B-instruct", None)?;
+    let mut tokenizer = load_tokenizer(&model_root)?;
+    // Qwen2 updates the post processor manually instead of into the tokenizer.json...
+    // https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct/blob/main/tokenization_qwen.py#L246
+    let template = TemplateProcessing::builder()
+        .try_single("$A:0 <|endoftext|>:0")
+        .unwrap()
+        .try_pair("$A:0 <|endoftext|>:0 $B:1 <|endoftext|>:1")
+        .unwrap()
+        .special_tokens(vec![("<|endoftext|>", 151643)])
+        .build()
+        .unwrap();
+    match tokenizer.get_post_processor() {
+        None => tokenizer.with_post_processor(template),
+        Some(post_processor) => {
+            let post_processor = Sequence::new(vec![
+                post_processor.clone(),
+                PostProcessorWrapper::Template(template),
+            ]);
+            tokenizer.with_post_processor(post_processor)
+        }
+    };
+
+    let backend = CandleBackend::new(
+        model_root,
+        "float16".to_string(),
+        ModelType::Embedding(Pool::LastToken),
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 1, 2].to_vec(),
+        vec![],
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("qwen2_batch", embeddings_batch, &matcher);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+
+    insta::assert_yaml_snapshot!("qwen2_single", embeddings_single, &matcher);
+    assert_eq!(embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(embeddings_batch[2], embeddings_single[0]);
+
+    Ok(())
+}
diff --git a/backends/candle/tests/test_jina.rs b/backends/candle/tests/test_jina.rs
index 4cd7bba6..4aa30d03 100644
--- a/backends/candle/tests/test_jina.rs
+++ b/backends/candle/tests/test_jina.rs
@@ -1,8 +1,8 @@
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -27,10 +27,10 @@ fn test_jina_small() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("jina_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -40,7 +40,7 @@ fn test_jina_small() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("jina_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_jina_code.rs b/backends/candle/tests/test_jina_code.rs
index 70248e1a..6c3b3f20 100644
--- a/backends/candle/tests/test_jina_code.rs
+++ b/backends/candle/tests/test_jina_code.rs
@@ -1,8 +1,8 @@
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -27,10 +27,10 @@ fn test_jina_code_base() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("jina_code_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -40,7 +40,7 @@ fn test_jina_code_base() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("jina_code_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_nomic.rs b/backends/candle/tests/test_nomic.rs
index 914be7ea..ce0a4559 100644
--- a/backends/candle/tests/test_nomic.rs
+++ b/backends/candle/tests/test_nomic.rs
@@ -1,8 +1,8 @@
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -27,10 +27,10 @@ fn test_nomic_small() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("nomic_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -40,7 +40,7 @@ fn test_nomic_small() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("nomic_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/core/Cargo.toml b/backends/core/Cargo.toml
index 5e330715..754f9526 100644
--- a/backends/core/Cargo.toml
+++ b/backends/core/Cargo.toml
@@ -6,9 +6,9 @@ authors.workspace = true
 homepage.workspace = true
 
 [dependencies]
-thiserror = "^1.0"
-clap = { version = "^4.1", features = ["derive"], optional = true }
-nohash-hasher = "^0.2"
+thiserror = { workspace = true }
+clap = { workspace = true, optional = true }
+nohash-hasher = { workspace = true }
 
 [features]
 clap = ["dep:clap"]
diff --git a/backends/core/src/lib.rs b/backends/core/src/lib.rs
index 06cef3ed..932c0083 100644
--- a/backends/core/src/lib.rs
+++ b/backends/core/src/lib.rs
@@ -63,6 +63,8 @@ pub enum Pool {
     /// This option is only available if the loaded model is a `ForMaskedLM` Transformer
     /// model.
     Splade,
+    /// Select the last token as embedding
+    LastToken,
 }
 
 impl fmt::Display for Pool {
@@ -71,6 +73,7 @@ impl fmt::Display for Pool {
             Pool::Cls => write!(f, "cls"),
             Pool::Mean => write!(f, "mean"),
             Pool::Splade => write!(f, "splade"),
+            Pool::LastToken => write!(f, "last_token"),
         }
     }
 }
diff --git a/backends/ort/Cargo.toml b/backends/ort/Cargo.toml
new file mode 100644
index 00000000..40ccc560
--- /dev/null
+++ b/backends/ort/Cargo.toml
@@ -0,0 +1,18 @@
+[package]
+name = "text-embeddings-backend-ort"
+version.workspace = true
+edition.workspace = true
+authors.workspace = true
+homepage.workspace = true
+
+[dependencies]
+anyhow = { workspace = true }
+nohash-hasher = { workspace = true }
+ndarray = "0.16.1"
+num_cpus = { workspace = true }
+ort = { version = "2.0.0-rc.8", default-features = false, features = ["download-binaries", "half", "onednn", "ndarray"] }
+text-embeddings-backend-core = { path = "../core" }
+tracing = { workspace = true }
+thiserror = { workspace = true }
+serde = { workspace = true }
+serde_json = { workspace = true }
diff --git a/backends/ort/src/lib.rs b/backends/ort/src/lib.rs
new file mode 100644
index 00000000..ab38fcc1
--- /dev/null
+++ b/backends/ort/src/lib.rs
@@ -0,0 +1,401 @@
+use ndarray::{s, Axis};
+use nohash_hasher::BuildNoHashHasher;
+use ort::{GraphOptimizationLevel, Session};
+use std::collections::HashMap;
+use std::ops::{Div, Mul};
+use std::path::PathBuf;
+use text_embeddings_backend_core::{
+    Backend, BackendError, Batch, Embedding, Embeddings, ModelType, Pool, Predictions,
+};
+
+pub struct OrtBackend {
+    session: Session,
+    pool: Pool,
+    type_id_name: Option<String>,
+}
+
+impl OrtBackend {
+    pub fn new(
+        model_path: PathBuf,
+        dtype: String,
+        model_type: ModelType,
+    ) -> Result<Self, BackendError> {
+        // Check dtype
+        if &dtype == "float32" {
+        } else {
+            return Err(BackendError::Start(format!(
+                "DType {dtype} is not supported"
+            )));
+        };
+
+        // Check model type
+        let pool = match model_type {
+            ModelType::Classifier => Pool::Cls,
+            ModelType::Embedding(pool) => match pool {
+                Pool::Splade | Pool::LastToken => {
+                    return Err(BackendError::Start(format!(
+                        "Pooling {pool} is not supported for this backend. Use `candle` backend instead."
+                    )));
+                }
+                pool => pool,
+            },
+        };
+
+        // Get model path
+        let onnx_path = {
+            let default_path = model_path.join("model.onnx");
+            match default_path.exists() {
+                true => default_path,
+                false => model_path.join("onnx/model.onnx"),
+            }
+        };
+
+        // Start onnx session
+        let session = Session::builder()
+            .s()?
+            .with_intra_threads(num_cpus::get())
+            .s()?
+            .with_optimization_level(GraphOptimizationLevel::Level3)
+            .s()?
+            .commit_from_file(onnx_path)
+            .s()?;
+
+        // Check if the model requires type tokens
+        let mut type_id_name = None;
+        for input in &session.inputs {
+            if &input.name == "token_type_ids" || &input.name == "input_type" {
+                type_id_name = Some(input.name.clone());
+                break;
+            }
+        }
+
+        Ok(Self {
+            session,
+            pool,
+            type_id_name,
+        })
+    }
+}
+
+impl Backend for OrtBackend {
+    fn max_batch_size(&self) -> Option<usize> {
+        Some(8)
+    }
+
+    fn health(&self) -> Result<(), BackendError> {
+        Ok(())
+    }
+
+    fn is_padded(&self) -> bool {
+        true
+    }
+
+    fn embed(&self, batch: Batch) -> Result<Embeddings, BackendError> {
+        let batch_size = batch.len();
+        let max_length = batch.max_length as usize;
+
+        // Whether a least one of the request in the batch is padded
+        let mut masking = false;
+
+        let (input_ids, type_ids, input_lengths, attention_mask) = {
+            let elems = batch_size * max_length;
+
+            if batch_size > 1 {
+                // Prepare padded batch
+                let mut input_ids = Vec::with_capacity(elems);
+                let mut type_ids = Vec::with_capacity(elems);
+                let mut attention_mask = Vec::with_capacity(elems);
+                let mut input_lengths = Vec::with_capacity(batch_size);
+
+                for i in 0..batch_size {
+                    let start = batch.cumulative_seq_lengths[i] as usize;
+                    let end = batch.cumulative_seq_lengths[i + 1] as usize;
+                    let seq_length = (end - start) as u32;
+                    input_lengths.push(seq_length as f32);
+
+                    // Copy values
+                    for j in start..end {
+                        input_ids.push(batch.input_ids[j] as i64);
+                        type_ids.push(batch.token_type_ids[j] as i64);
+                        attention_mask.push(1_i64);
+                    }
+
+                    // Add padding if needed
+                    let padding = batch.max_length - seq_length;
+                    if padding > 0 {
+                        // Set bool to use attention mask
+                        masking = true;
+                        for _ in 0..padding {
+                            input_ids.push(0);
+                            type_ids.push(0);
+                            attention_mask.push(0_i64);
+                        }
+                    }
+                }
+                (input_ids, type_ids, input_lengths, attention_mask)
+            } else {
+                let attention_mask = vec![1_i64; elems];
+
+                (
+                    batch.input_ids.into_iter().map(|v| v as i64).collect(),
+                    batch.token_type_ids.into_iter().map(|v| v as i64).collect(),
+                    vec![batch.max_length as f32],
+                    attention_mask,
+                )
+            }
+        };
+
+        // Create ndarrays
+        let input_ids = ndarray::Array2::from_shape_vec((batch_size, max_length), input_ids).e()?;
+        let attention_mask =
+            ndarray::Array2::from_shape_vec((batch_size, max_length), attention_mask).e()?;
+        let input_lengths = ndarray::Array1::from_vec(input_lengths);
+
+        // Create onnx inputs
+        let inputs = match self.type_id_name.as_ref() {
+            Some(type_id_name) => {
+                // Add type ids to inputs
+                let type_ids =
+                    ndarray::Array2::from_shape_vec((batch_size, max_length), type_ids).e()?;
+                ort::inputs!["input_ids" => input_ids, "attention_mask" => attention_mask.clone(), type_id_name => type_ids].e()?
+            }
+            None => {
+                ort::inputs!["input_ids" => input_ids, "attention_mask" => attention_mask.clone()]
+                    .e()?
+            }
+        };
+
+        // Run model
+        let outputs = self.session.run(inputs).e()?;
+        // Get last_hidden_state ndarray
+
+        let outputs = outputs
+            .get("last_hidden_state")
+            .or(outputs.get("token_embeddings"))
+            .ok_or(BackendError::Inference(format!(
+                "Unknown output keys: {:?}",
+                self.session.outputs
+            )))?
+            .try_extract_tensor::<f32>()
+            .e()?
+            .to_owned();
+
+        // Final embeddings struct
+        let mut embeddings =
+            HashMap::with_capacity_and_hasher(batch_size, BuildNoHashHasher::default());
+
+        let has_pooling_requests = !batch.pooled_indices.is_empty();
+        let has_raw_requests = !batch.raw_indices.is_empty();
+
+        if has_pooling_requests {
+            let mut outputs = outputs.clone();
+
+            // Only use pooled_indices if at least one member of the batch ask for raw embeddings
+            let indices = if has_raw_requests {
+                let indices: Vec<usize> =
+                    batch.pooled_indices.iter().map(|v| *v as usize).collect();
+
+                // Select values in the batch
+                outputs = outputs.select(Axis(0), &indices);
+                Some(indices)
+            } else {
+                None
+            };
+
+            let pooled_embeddings = match self.pool {
+                // CLS pooling
+                Pool::Cls => outputs.slice(s![.., 0, ..]).into_owned().into_dyn(),
+                // Last token pooling is not supported for this model
+                Pool::LastToken => unreachable!(),
+                // Mean pooling
+                Pool::Mean => {
+                    if masking {
+                        let mut attention_mask = attention_mask;
+                        let mut input_lengths = input_lengths;
+
+                        if let Some(indices) = indices {
+                            // Select values in the batch
+                            attention_mask = attention_mask.select(Axis(0), &indices);
+                            input_lengths = input_lengths.select(Axis(0), &indices);
+                        };
+
+                        // Cast and reshape
+                        let attention_mask = attention_mask.mapv(|x| x as f32).insert_axis(Axis(2));
+
+                        // Mask padded values
+                        outputs = outputs.mul(attention_mask);
+                        outputs
+                            .sum_axis(Axis(1))
+                            .div(input_lengths.insert_axis(Axis(1)))
+                    } else {
+                        outputs.mean_axis(Axis(1)).unwrap()
+                    }
+                }
+                Pool::Splade => unreachable!(),
+            };
+
+            for (i, e) in batch
+                .pooled_indices
+                .into_iter()
+                .zip(pooled_embeddings.rows())
+            {
+                embeddings.insert(i as usize, Embedding::Pooled(e.to_vec()));
+            }
+        };
+
+        if has_raw_requests {
+            // Reshape outputs
+            let s = outputs.shape().to_vec();
+            let outputs = outputs.into_shape_with_order((s[0] * s[1], s[2])).e()?;
+
+            // We need to remove the padding tokens only if batch_size > 1 and there are some
+            // member of the batch that require pooling
+            // or if batch_size > 1 and the members of the batch have different lengths
+            let raw_embeddings = if (masking || has_pooling_requests) && batch_size > 1 {
+                let mut final_indices: Vec<usize> = Vec::with_capacity(batch_size * max_length);
+
+                for i in batch.raw_indices.iter() {
+                    let start = i * batch.max_length;
+                    let i = *i as usize;
+                    let length =
+                        batch.cumulative_seq_lengths[i + 1] - batch.cumulative_seq_lengths[i];
+
+                    for j in start..start + length {
+                        // Add indices for the tokens of this specific member of the batch
+                        final_indices.push(j as usize);
+                    }
+                }
+
+                // Select the tokens with final indices
+                outputs.select(Axis(0), &final_indices)
+            } else {
+                outputs
+            };
+
+            // Used for indexing in the raw_embeddings tensor
+            let input_lengths: Vec<usize> = (0..batch_size)
+                .map(|i| {
+                    (batch.cumulative_seq_lengths[i + 1] - batch.cumulative_seq_lengths[i]) as usize
+                })
+                .collect();
+
+            let mut cumulative_length = 0;
+            for i in batch.raw_indices.into_iter() {
+                let length = input_lengths[i as usize];
+                let e = raw_embeddings.slice(s![cumulative_length..cumulative_length + length, ..]);
+                let e = e.rows().into_iter().map(|v| v.to_vec()).collect();
+
+                embeddings.insert(i as usize, Embedding::All(e));
+                cumulative_length += length;
+            }
+        }
+
+        Ok(embeddings)
+    }
+
+    fn predict(&self, batch: Batch) -> Result<Predictions, BackendError> {
+        let batch_size = batch.len();
+        let max_length = batch.max_length as usize;
+
+        let (input_ids, type_ids, attention_mask) = {
+            let elems = batch_size * max_length;
+
+            if batch_size > 1 {
+                // Prepare padded batch
+                let mut input_ids = Vec::with_capacity(elems);
+                let mut type_ids = Vec::with_capacity(elems);
+                let mut attention_mask = Vec::with_capacity(elems);
+
+                for i in 0..batch_size {
+                    let start = batch.cumulative_seq_lengths[i] as usize;
+                    let end = batch.cumulative_seq_lengths[i + 1] as usize;
+                    let seq_length = (end - start) as u32;
+
+                    // Copy values
+                    for j in start..end {
+                        input_ids.push(batch.input_ids[j] as i64);
+                        type_ids.push(batch.token_type_ids[j] as i64);
+                        attention_mask.push(1_i64);
+                    }
+
+                    // Add padding if needed
+                    let padding = batch.max_length - seq_length;
+                    if padding > 0 {
+                        for _ in 0..padding {
+                            input_ids.push(0);
+                            type_ids.push(0);
+                            attention_mask.push(0_i64);
+                        }
+                    }
+                }
+                (input_ids, type_ids, attention_mask)
+            } else {
+                let attention_mask = vec![1_i64; elems];
+
+                (
+                    batch.input_ids.into_iter().map(|v| v as i64).collect(),
+                    batch.token_type_ids.into_iter().map(|v| v as i64).collect(),
+                    attention_mask,
+                )
+            }
+        };
+
+        // Create ndarrays
+        let input_ids = ndarray::Array2::from_shape_vec((batch_size, max_length), input_ids).e()?;
+        let attention_mask =
+            ndarray::Array2::from_shape_vec((batch_size, max_length), attention_mask).e()?;
+
+        // Create onnx inputs
+        let inputs = match self.type_id_name.as_ref() {
+            Some(type_id_name) => {
+                // Add type ids to inputs
+                let type_ids =
+                    ndarray::Array2::from_shape_vec((batch_size, max_length), type_ids).e()?;
+                ort::inputs!["input_ids" => input_ids, "attention_mask" => attention_mask.clone(), type_id_name => type_ids].e()?
+            }
+            None => {
+                ort::inputs!["input_ids" => input_ids, "attention_mask" => attention_mask.clone()]
+                    .e()?
+            }
+        };
+
+        // Run model
+        let outputs = self.session.run(inputs).e()?;
+        // Get last_hidden_state ndarray
+        let outputs = outputs["logits"]
+            .try_extract_tensor::<f32>()
+            .e()?
+            .to_owned();
+
+        let mut predictions =
+            HashMap::with_capacity_and_hasher(batch_size, BuildNoHashHasher::default());
+        for (i, r) in outputs.rows().into_iter().enumerate() {
+            predictions.insert(i, r.to_vec());
+        }
+
+        Ok(predictions)
+    }
+}
+
+pub trait WrapErr<O> {
+    fn s(self) -> Result<O, BackendError>;
+    fn e(self) -> Result<O, BackendError>;
+}
+
+impl<O> WrapErr<O> for Result<O, ort::Error> {
+    fn s(self) -> Result<O, BackendError> {
+        self.map_err(|e| BackendError::Start(e.to_string()))
+    }
+    fn e(self) -> Result<O, BackendError> {
+        self.map_err(|e| BackendError::Inference(e.to_string()))
+    }
+}
+
+impl<O> WrapErr<O> for Result<O, ndarray::ShapeError> {
+    fn s(self) -> Result<O, BackendError> {
+        self.map_err(|e| BackendError::Start(e.to_string()))
+    }
+    fn e(self) -> Result<O, BackendError> {
+        self.map_err(|e| BackendError::Inference(e.to_string()))
+    }
+}
diff --git a/backends/src/dtype.rs b/backends/src/dtype.rs
index d2c896ce..53193ef7 100644
--- a/backends/src/dtype.rs
+++ b/backends/src/dtype.rs
@@ -12,11 +12,8 @@ pub enum DType {
         all(feature = "candle", not(feature = "accelerate"))
     ))]
     Float16,
-    // Float32 is not available on candle cuda
-    #[cfg(any(feature = "python", feature = "candle"))]
+    #[cfg(any(feature = "python", feature = "candle", feature = "ort"))]
     Float32,
-    // #[cfg(feature = "candle")]
-    // Q6K,
 }
 
 impl fmt::Display for DType {
@@ -28,11 +25,32 @@ impl fmt::Display for DType {
                 all(feature = "candle", not(feature = "accelerate"))
             ))]
             DType::Float16 => write!(f, "float16"),
-            // Float32 is not available on candle cuda
-            #[cfg(any(feature = "python", feature = "candle"))]
+            #[cfg(any(feature = "python", feature = "candle", feature = "ort"))]
             DType::Float32 => write!(f, "float32"),
-            // #[cfg(feature = "candle")]
-            // DType::Q6K => write!(f, "q6k"),
+        }
+    }
+}
+
+#[allow(clippy::derivable_impls)]
+impl Default for DType {
+    fn default() -> Self {
+        #[cfg(any(
+            feature = "accelerate",
+            feature = "mkl",
+            feature = "mkl-dynamic",
+            feature = "ort"
+        ))]
+        {
+            DType::Float32
+        }
+        #[cfg(not(any(
+            feature = "accelerate",
+            feature = "mkl",
+            feature = "mkl-dynamic",
+            feature = "ort"
+        )))]
+        {
+            DType::Float16
         }
     }
 }
diff --git a/backends/src/lib.rs b/backends/src/lib.rs
index d332b4a7..9d44cdbd 100644
--- a/backends/src/lib.rs
+++ b/backends/src/lib.rs
@@ -1,5 +1,7 @@
 mod dtype;
 
+use hf_hub::api::tokio::{ApiError, ApiRepo};
+use std::cmp::{max, min};
 use std::path::PathBuf;
 use std::sync::Arc;
 use std::thread::JoinHandle;
@@ -16,13 +18,16 @@ pub use text_embeddings_backend_core::{
 #[cfg(feature = "candle")]
 use text_embeddings_backend_candle::CandleBackend;
 
+#[cfg(feature = "ort")]
+use text_embeddings_backend_ort::OrtBackend;
+
 #[cfg(feature = "python")]
 use text_embeddings_backend_python::PythonBackend;
 
 #[derive(Debug, Clone)]
 pub struct Backend {
     /// Channel to communicate with the background thread
-    backend_sender: mpsc::UnboundedSender<BackendCommand>,
+    backend_sender: mpsc::Sender<BackendCommand>,
     /// Health status
     health_receiver: watch::Receiver<bool>,
     _backend_thread: Arc<BackendThread>,
@@ -40,7 +45,7 @@ impl Backend {
         otlp_endpoint: Option<String>,
         otlp_service_name: String,
     ) -> Result<Self, BackendError> {
-        let (backend_sender, backend_receiver) = mpsc::unbounded_channel();
+        let (backend_sender, backend_receiver) = mpsc::channel(8);
 
         let backend = init_backend(
             model_path,
@@ -67,6 +72,62 @@ impl Backend {
         })
     }
 
+    #[instrument(skip(self))]
+    pub async fn warmup(
+        &self,
+        max_input_length: usize,
+        max_batch_tokens: usize,
+        max_batch_requests: Option<usize>,
+    ) -> Result<(), BackendError> {
+        let mut input_ids = Vec::with_capacity(max_batch_tokens);
+        let mut token_type_ids = Vec::with_capacity(max_batch_tokens);
+        let mut position_ids = Vec::with_capacity(max_batch_tokens);
+
+        let mut cumulative_seq_lengths = vec![0];
+        let mut pooled_indices = Vec::new();
+
+        let mut i = 0_u32;
+        let mut remaining = max_batch_tokens;
+        let mut cumulative_length = 0;
+        let mut max_length = 0;
+
+        while remaining > 0 {
+            let request_length = min(remaining, max_input_length);
+            cumulative_length += request_length;
+            max_length = max(max_length, request_length as u32);
+
+            input_ids.extend(vec![0; request_length]);
+            token_type_ids.extend(vec![0; request_length]);
+            position_ids.extend((0..request_length as u32).collect::<Vec<u32>>());
+
+            cumulative_seq_lengths.push(cumulative_length as u32);
+            pooled_indices.push(i);
+
+            i += 1;
+            remaining = remaining.saturating_sub(max_input_length);
+            if let Some(max_batch_requests) = &max_batch_requests {
+                if i as usize == *max_batch_requests {
+                    break;
+                }
+            }
+        }
+
+        let batch = Batch {
+            input_ids,
+            token_type_ids,
+            position_ids,
+            cumulative_seq_lengths,
+            max_length,
+            pooled_indices,
+            raw_indices: vec![],
+        };
+
+        match &self.model_type {
+            ModelType::Classifier => self.predict(batch).await.map(|_| ()),
+            ModelType::Embedding(_) => self.embed(batch).await.map(|_| ()),
+        }
+    }
+
     #[instrument(skip(self))]
     pub async fn health(&self) -> Result<(), BackendError> {
         if *self.health_receiver.borrow() {
@@ -76,6 +137,7 @@ impl Backend {
             let (sender, receiver) = oneshot::channel();
             self.backend_sender
                 .send(BackendCommand::Health(Span::current(), sender))
+                .await
                 .expect("No backend receiver. This is a bug.");
             receiver.await.expect(
                 "Backend blocking task dropped the sender without sending a response. This is a bug.",
@@ -110,7 +172,7 @@ impl Backend {
         let (sender, receiver) = oneshot::channel();
 
         self.backend_sender
-            .send(BackendCommand::Embed(batch, Span::current(), sender))
+            .try_send(BackendCommand::Embed(batch, Span::current(), sender))
             .expect("No backend receiver. This is a bug.");
         receiver.await.expect(
             "Backend blocking task dropped the sender without send a response. This is a bug.",
@@ -122,7 +184,7 @@ impl Backend {
         let (sender, receiver) = oneshot::channel();
 
         self.backend_sender
-            .send(BackendCommand::Predict(batch, Span::current(), sender))
+            .try_send(BackendCommand::Predict(batch, Span::current(), sender))
             .expect("No backend receiver. This is a bug.");
         receiver.await.expect(
             "Backend blocking task dropped the sender without send a response. This is a bug.",
@@ -164,6 +226,13 @@ fn init_backend(
                 .expect("Python Backend management thread failed")?,
             ));
         }
+    } else if cfg!(feature = "ort") {
+        #[cfg(feature = "ort")]
+        return Ok(Box::new(OrtBackend::new(
+            model_path,
+            dtype.to_string(),
+            model_type,
+        )?));
     }
     Err(BackendError::NoBackend)
 }
@@ -174,7 +243,7 @@ struct BackendThread(Option<JoinHandle<()>>);
 impl BackendThread {
     fn new(
         backend: Box<dyn CoreBackend + Send>,
-        mut backend_receiver: mpsc::UnboundedReceiver<BackendCommand>,
+        mut backend_receiver: mpsc::Receiver<BackendCommand>,
         health_sender: watch::Sender<bool>,
     ) -> Self {
         let handle = std::thread::spawn(move || {
@@ -228,3 +297,98 @@ enum BackendCommand {
         oneshot::Sender<Result<(Predictions, Duration), BackendError>>,
     ),
 }
+
+pub async fn download_weights(api: &ApiRepo) -> Result<Vec<PathBuf>, ApiError> {
+    let model_files = if cfg!(feature = "python") || cfg!(feature = "candle") {
+        match download_safetensors(api).await {
+            Ok(p) => p,
+            Err(_) => {
+                tracing::warn!("safetensors weights not found. Using `pytorch_model.bin` instead. Model loading will be significantly slower.");
+                tracing::info!("Downloading `pytorch_model.bin`");
+                let p = api.get("pytorch_model.bin").await?;
+                vec![p]
+            }
+        }
+    } else if cfg!(feature = "ort") {
+        match download_onnx(api).await {
+            Ok(p) => p,
+            Err(err) => {
+                panic!("failed to download `model.onnx` or `model.onnx_data`. Check the onnx file exists in the repository. {err}");
+            }
+        }
+    } else {
+        unreachable!()
+    };
+
+    Ok(model_files)
+}
+
+async fn download_safetensors(api: &ApiRepo) -> Result<Vec<PathBuf>, ApiError> {
+    // Single file
+    tracing::info!("Downloading `model.safetensors`");
+    match api.get("model.safetensors").await {
+        Ok(p) => return Ok(vec![p]),
+        Err(err) => tracing::warn!("Could not download `model.safetensors`: {}", err),
+    };
+
+    // Sharded weights
+    // Download and parse index file
+    tracing::info!("Downloading `model.safetensors.index.json`");
+    let index_file = api.get("model.safetensors.index.json").await?;
+    let index_file_string: String =
+        std::fs::read_to_string(index_file).expect("model.safetensors.index.json is corrupted");
+    let json: serde_json::Value = serde_json::from_str(&index_file_string)
+        .expect("model.safetensors.index.json is corrupted");
+
+    let weight_map = match json.get("weight_map") {
+        Some(serde_json::Value::Object(map)) => map,
+        _ => panic!("model.safetensors.index.json is corrupted"),
+    };
+
+    let mut safetensors_filenames = std::collections::HashSet::new();
+    for value in weight_map.values() {
+        if let Some(file) = value.as_str() {
+            safetensors_filenames.insert(file.to_string());
+        }
+    }
+
+    // Download weight files
+    let mut safetensors_files = Vec::new();
+    for n in safetensors_filenames {
+        tracing::info!("Downloading `{}`", n);
+        safetensors_files.push(api.get(&n).await?);
+    }
+
+    Ok(safetensors_files)
+}
+
+async fn download_onnx(api: &ApiRepo) -> Result<Vec<PathBuf>, ApiError> {
+    let mut model_files: Vec<PathBuf> = Vec::new();
+
+    tracing::info!("Downloading `model.onnx`");
+    match api.get("model.onnx").await {
+        Ok(p) => model_files.push(p),
+        Err(err) => {
+            tracing::warn!("Could not download `model.onnx`: {err}");
+            tracing::info!("Downloading `onnx/model.onnx`");
+            let p = api.get("onnx/model.onnx").await?;
+            model_files.push(p.parent().unwrap().to_path_buf())
+        }
+    };
+
+    tracing::info!("Downloading `model.onnx_data`");
+    match api.get("model.onnx_data").await {
+        Ok(p) => model_files.push(p),
+        Err(err) => {
+            tracing::warn!("Could not download `model.onnx_data`: {err}");
+            tracing::info!("Downloading `onnx/model.onnx_data`");
+
+            match api.get("onnx/model.onnx_data").await {
+                Ok(p) => model_files.push(p.parent().unwrap().to_path_buf()),
+                Err(err) => tracing::warn!("Could not download `onnx/model.onnx_data`: {err}"),
+            }
+        }
+    }
+
+    Ok(model_files)
+}
diff --git a/core/Cargo.toml b/core/Cargo.toml
index 0e83c744..d69871dc 100644
--- a/core/Cargo.toml
+++ b/core/Cargo.toml
@@ -6,10 +6,12 @@ authors.workspace = true
 homepage.workspace = true
 
 [dependencies]
-hf-hub = { version = "^0.3.0", features = ["tokio"], default-features = false }
-metrics = "^0.21"
+async-channel = "^2.3"
+hf-hub = { workspace = true }
+metrics = { workspace = true }
+serde_json = { workspace = true }
 text-embeddings-backend = { path = "../backends" }
-thiserror = "^1.0"
-tokenizers = { version = "^0.19.1", default-features = false, features = ["onig", "esaxx_fast"] }
-tracing = "^0.1"
-tokio = { version = "^1.25", features = ["rt", "rt-multi-thread", "parking_lot", "sync"] }
+thiserror = { workspace = true }
+tokenizers = { workspace = true }
+tracing = { workspace = true }
+tokio = { workspace = true }
diff --git a/core/src/download.rs b/core/src/download.rs
index 6cc60472..02ea484d 100644
--- a/core/src/download.rs
+++ b/core/src/download.rs
@@ -1,5 +1,6 @@
 use hf_hub::api::tokio::{ApiError, ApiRepo};
 use std::path::PathBuf;
+use text_embeddings_backend::download_weights;
 use tracing::instrument;
 
 // Old classes used other config names than 'sentence_bert_config.json'
@@ -19,20 +20,14 @@ pub async fn download_artifacts(api: &ApiRepo) -> Result<PathBuf, ApiError> {
 
     tracing::info!("Starting download");
 
+    tracing::info!("Downloading `config.json`");
     api.get("config.json").await?;
+
+    tracing::info!("Downloading `tokenizer.json`");
     api.get("tokenizer.json").await?;
 
-    let model_root = match api.get("model.safetensors").await {
-        Ok(p) => p,
-        Err(_) => {
-            let p = api.get("pytorch_model.bin").await?;
-            tracing::warn!("`model.safetensors` not found. Using `pytorch_model.bin` instead. Model loading will be significantly slower.");
-            p
-        }
-    }
-        .parent()
-        .unwrap()
-        .to_path_buf();
+    let model_files = download_weights(api).await?;
+    let model_root = model_files[0].parent().unwrap().to_path_buf();
 
     tracing::info!("Model artifacts downloaded in {:?}", start.elapsed());
     Ok(model_root)
@@ -40,6 +35,7 @@ pub async fn download_artifacts(api: &ApiRepo) -> Result<PathBuf, ApiError> {
 
 #[instrument(skip_all)]
 pub async fn download_pool_config(api: &ApiRepo) -> Result<PathBuf, ApiError> {
+    tracing::info!("Downloading `1_Pooling/config.json`");
     let pool_config_path = api.get("1_Pooling/config.json").await?;
     Ok(pool_config_path)
 }
@@ -60,3 +56,10 @@ pub async fn download_st_config(api: &ApiRepo) -> Result<PathBuf, ApiError> {
 
     Err(err)
 }
+
+#[instrument(skip_all)]
+pub async fn download_new_st_config(api: &ApiRepo) -> Result<PathBuf, ApiError> {
+    tracing::info!("Downloading `config_sentence_transformers.json`");
+    let pool_config_path = api.get("config_sentence_transformers.json").await?;
+    Ok(pool_config_path)
+}
diff --git a/core/src/infer.rs b/core/src/infer.rs
index 0f95ff8b..23b343bf 100644
--- a/core/src/infer.rs
+++ b/core/src/infer.rs
@@ -30,14 +30,10 @@ impl Infer {
     ) -> Self {
         let notify_batching_task = Arc::new(Notify::new());
 
-        let (embed_sender, embed_receiver) = mpsc::unbounded_channel();
+        // Bound channel to 1 to be able to prefetch one batch
+        let (embed_sender, embed_receiver) = mpsc::channel(1);
 
-        // Create two batching tasks to prefetch batches
-        tokio::spawn(batching_task(
-            queue.clone(),
-            notify_batching_task.clone(),
-            embed_sender.clone(),
-        ));
+        // Batching task
         tokio::spawn(batching_task(
             queue.clone(),
             notify_batching_task.clone(),
@@ -59,23 +55,25 @@ impl Infer {
         }
     }
 
-    #[instrument(skip(self))]
+    #[instrument(skip(self, inputs))]
     pub async fn tokenize<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
         add_special_tokens: bool,
-    ) -> Result<RawEncoding, TextEmbeddingsError> {
+        prompt_name: Option<String>,
+    ) -> Result<(Option<String>, RawEncoding), TextEmbeddingsError> {
         self.tokenization
-            .tokenize(inputs.into(), add_special_tokens)
+            .tokenize(inputs.into(), add_special_tokens, prompt_name)
             .await
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "tokenization");
+                let counter = metrics::counter!("te_request_failure", "err" => "tokenization");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })
     }
 
-    #[instrument(skip(self))]
+    #[instrument(skip(self, ids))]
     pub async fn decode(
         &self,
         ids: Vec<u32>,
@@ -85,7 +83,8 @@ impl Infer {
             .decode(ids, skip_special_tokens)
             .await
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "tokenization");
+                let counter = metrics::counter!("te_request_failure", "err" => "tokenization");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })
@@ -98,11 +97,13 @@ impl Infer {
             .limit_concurrent_requests
             .try_acquire_owned()
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "overloaded");
+                let counter = metrics::counter!("te_request_failure", "err" => "overloaded");
+                counter.increment(1);
                 tracing::error!("{err}");
                 TextEmbeddingsError::from(err)
             })
     }
+
     #[instrument(skip(self))]
     pub async fn acquire_permit(&self) -> OwnedSemaphorePermit {
         // Limit concurrent requests by acquiring a permit from the semaphore
@@ -113,18 +114,20 @@ impl Infer {
             .expect("Semaphore has been closed. This is a bug.")
     }
 
-    #[instrument(skip(self, permit))]
+    #[instrument(skip(self, inputs, permit))]
     pub async fn embed_all<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
         truncate: bool,
         truncation_direction: TruncationDirection,
+        prompt_name: Option<String>,
         permit: OwnedSemaphorePermit,
     ) -> Result<AllEmbeddingsInferResponse, TextEmbeddingsError> {
         let start_time = Instant::now();
 
         if self.is_splade() {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "`embed_all` is not available for SPLADE models".to_string();
             tracing::error!("{message}");
             return Err(TextEmbeddingsError::Backend(BackendError::Inference(
@@ -137,6 +140,7 @@ impl Infer {
                 inputs,
                 truncate,
                 truncation_direction,
+                prompt_name,
                 false,
                 &start_time,
                 permit,
@@ -151,36 +155,34 @@ impl Infer {
         let total_time = start_time.elapsed();
 
         // Metrics
-        metrics::increment_counter!("te_embed_success");
-        metrics::histogram!("te_embed_duration", total_time.as_secs_f64());
-        metrics::histogram!(
-            "te_embed_tokenization_duration",
-            response.metadata.tokenization.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_queue_duration",
-            response.metadata.queue.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_inference_duration",
-            response.metadata.inference.as_secs_f64()
-        );
+        let counter = metrics::counter!("te_embed_success");
+        counter.increment(1);
+        let histogram = metrics::histogram!("te_embed_duration");
+        histogram.record(total_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_tokenization_duration");
+        histogram.record(response.metadata.tokenization.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_queue_duration");
+        histogram.record(response.metadata.queue.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_inference_duration");
+        histogram.record(response.metadata.inference.as_secs_f64());
 
         Ok(response)
     }
 
-    #[instrument(skip(self, permit))]
+    #[instrument(skip(self, inputs, permit))]
     pub async fn embed_sparse<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
         truncate: bool,
         truncation_direction: TruncationDirection,
+        prompt_name: Option<String>,
         permit: OwnedSemaphorePermit,
     ) -> Result<PooledEmbeddingsInferResponse, TextEmbeddingsError> {
         let start_time = Instant::now();
 
         if !self.is_splade() {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "Model is not an embedding model with SPLADE pooling".to_string();
             tracing::error!("{message}");
             return Err(TextEmbeddingsError::Backend(BackendError::Inference(
@@ -193,6 +195,7 @@ impl Infer {
                 inputs,
                 truncate,
                 truncation_direction,
+                prompt_name,
                 true,
                 &start_time,
                 permit,
@@ -207,37 +210,35 @@ impl Infer {
         let total_time = start_time.elapsed();
 
         // Metrics
-        metrics::increment_counter!("te_embed_success");
-        metrics::histogram!("te_embed_duration", total_time.as_secs_f64());
-        metrics::histogram!(
-            "te_embed_tokenization_duration",
-            response.metadata.tokenization.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_queue_duration",
-            response.metadata.queue.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_inference_duration",
-            response.metadata.inference.as_secs_f64()
-        );
+        let counter = metrics::counter!("te_embed_success");
+        counter.increment(1);
+        let histogram = metrics::histogram!("te_embed_duration");
+        histogram.record(total_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_tokenization_duration");
+        histogram.record(response.metadata.tokenization.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_queue_duration");
+        histogram.record(response.metadata.queue.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_inference_duration");
+        histogram.record(response.metadata.inference.as_secs_f64());
 
         Ok(response)
     }
 
-    #[instrument(skip(self, permit))]
+    #[instrument(skip(self, inputs, permit))]
     pub async fn embed_pooled<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
         truncate: bool,
         truncation_direction: TruncationDirection,
+        prompt_name: Option<String>,
         normalize: bool,
         permit: OwnedSemaphorePermit,
     ) -> Result<PooledEmbeddingsInferResponse, TextEmbeddingsError> {
         let start_time = Instant::now();
 
         if self.is_splade() && normalize {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "`normalize` is not available for SPLADE models".to_string();
             tracing::error!("{message}");
             return Err(TextEmbeddingsError::Backend(BackendError::Inference(
@@ -250,6 +251,7 @@ impl Infer {
                 inputs,
                 truncate,
                 truncation_direction,
+                prompt_name,
                 true,
                 &start_time,
                 permit,
@@ -281,35 +283,34 @@ impl Infer {
         let total_time = start_time.elapsed();
 
         // Metrics
-        metrics::increment_counter!("te_embed_success");
-        metrics::histogram!("te_embed_duration", total_time.as_secs_f64());
-        metrics::histogram!(
-            "te_embed_tokenization_duration",
-            response.metadata.tokenization.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_queue_duration",
-            response.metadata.queue.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_inference_duration",
-            response.metadata.inference.as_secs_f64()
-        );
+        let counter = metrics::counter!("te_embed_success");
+        counter.increment(1);
+        let histogram = metrics::histogram!("te_embed_duration");
+        histogram.record(total_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_tokenization_duration");
+        histogram.record(response.metadata.tokenization.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_queue_duration");
+        histogram.record(response.metadata.queue.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_inference_duration");
+        histogram.record(response.metadata.inference.as_secs_f64());
 
         Ok(response)
     }
 
+    #[allow(clippy::too_many_arguments)]
     async fn embed<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
         truncate: bool,
         truncation_direction: TruncationDirection,
+        prompt_name: Option<String>,
         pooling: bool,
         start_time: &Instant,
         _permit: OwnedSemaphorePermit,
     ) -> Result<InferResult, TextEmbeddingsError> {
         if self.is_classifier() {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "Model is not an embedding model".to_string();
             tracing::error!("{message}");
             return Err(TextEmbeddingsError::Backend(BackendError::Inference(
@@ -317,15 +318,17 @@ impl Infer {
             )));
         }
 
-        metrics::increment_counter!("te_embed_count");
+        let counter = metrics::counter!("te_embed_count");
+        counter.increment(1);
 
         // Tokenization
         let encoding = self
             .tokenization
-            .encode(inputs.into(), truncate, truncation_direction)
+            .encode(inputs.into(), truncate, truncation_direction, prompt_name)
             .await
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "tokenization");
+                let counter = metrics::counter!("te_request_failure", "err" => "tokenization");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })?;
@@ -353,7 +356,8 @@ impl Infer {
                 "Infer batching task dropped the sender without sending a response. This is a bug.",
             )
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "inference");
+                let counter = metrics::counter!("te_request_failure", "err" => "inference");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })?;
@@ -361,7 +365,7 @@ impl Infer {
         Ok(response)
     }
 
-    #[instrument(skip(self, _permit))]
+    #[instrument(skip(self, inputs, _permit))]
     pub async fn predict<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
@@ -371,7 +375,8 @@ impl Infer {
         _permit: OwnedSemaphorePermit,
     ) -> Result<ClassificationInferResponse, TextEmbeddingsError> {
         if !self.is_classifier() {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "Model is not a classifier model".to_string();
             return Err(TextEmbeddingsError::Backend(BackendError::Inference(
                 message,
@@ -379,15 +384,17 @@ impl Infer {
         }
 
         let start_time = Instant::now();
-        metrics::increment_counter!("te_predict_count");
+        let counter = metrics::counter!("te_predict_count");
+        counter.increment(1);
 
         // Tokenization
         let encoding = self
             .tokenization
-            .encode(inputs.into(), truncate, truncation_direction)
+            .encode(inputs.into(), truncate, truncation_direction, None)
             .await
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "tokenization");
+                let counter = metrics::counter!("te_request_failure", "err" => "tokenization");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })?;
@@ -415,7 +422,8 @@ impl Infer {
                 "Infer batching task dropped the sender without sending a response. This is a bug.",
             )
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "inference");
+                let counter = metrics::counter!("te_request_failure", "err" => "inference");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })?;
@@ -452,20 +460,16 @@ impl Infer {
         let total_time = start_time.elapsed();
 
         // Metrics
-        metrics::increment_counter!("te_predict_success");
-        metrics::histogram!("te_predict_duration", total_time.as_secs_f64());
-        metrics::histogram!(
-            "te_predict_tokenization_duration",
-            response.metadata.tokenization.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_predict_queue_duration",
-            response.metadata.queue.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_predict_inference_duration",
-            response.metadata.inference.as_secs_f64()
-        );
+        let counter = metrics::counter!("te_predict_success");
+        counter.increment(1);
+        let histogram = metrics::histogram!("te_predict_duration");
+        histogram.record(total_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_predict_tokenization_duration");
+        histogram.record(response.metadata.tokenization.as_secs_f64());
+        let histogram = metrics::histogram!("te_predict_queue_duration");
+        histogram.record(response.metadata.queue.as_secs_f64());
+        let histogram = metrics::histogram!("te_predict_inference_duration");
+        histogram.record(response.metadata.inference.as_secs_f64());
 
         Ok(response)
     }
@@ -495,30 +499,30 @@ impl Infer {
 }
 
 #[instrument(skip_all)]
-async fn batching_task(
-    queue: Queue,
-    notify: Arc<Notify>,
-    embed_sender: mpsc::UnboundedSender<(NextBatch, oneshot::Sender<()>)>,
-) {
+async fn batching_task(queue: Queue, notify: Arc<Notify>, embed_sender: mpsc::Sender<NextBatch>) {
     loop {
         notify.notified().await;
 
-        while let Some(next_batch) = queue.next_batch().await {
-            let (callback_sender, callback_receiver) = oneshot::channel();
-            embed_sender
-                .send((next_batch, callback_sender))
+        {
+            let mut permit = embed_sender
+                .reserve()
+                .await
                 .expect("embed receiver was dropped. This is a bug.");
-            let _ = callback_receiver.await;
+
+            while let Some(next_batch) = queue.next_batch().await {
+                permit.send(next_batch);
+                permit = embed_sender
+                    .reserve()
+                    .await
+                    .expect("embed receiver was dropped. This is a bug.");
+            }
         }
     }
 }
 
 #[instrument(skip_all)]
-async fn backend_task(
-    backend: Backend,
-    mut embed_receiver: mpsc::UnboundedReceiver<(NextBatch, oneshot::Sender<()>)>,
-) {
-    while let Some((batch, _callback)) = embed_receiver.recv().await {
+async fn backend_task(backend: Backend, mut embed_receiver: mpsc::Receiver<NextBatch>) {
+    while let Some(batch) = embed_receiver.recv().await {
         match &backend.model_type {
             ModelType::Classifier => {
                 let results = backend.predict(batch.1).await;
diff --git a/core/src/queue.rs b/core/src/queue.rs
index dfefda21..3fd8b771 100644
--- a/core/src/queue.rs
+++ b/core/src/queue.rs
@@ -35,7 +35,7 @@ pub struct Metadata {
 #[derive(Debug, Clone)]
 pub struct Queue {
     /// Channel to communicate with the background queue task
-    queue_sender: mpsc::UnboundedSender<QueueCommand>,
+    queue_sender: mpsc::Sender<QueueCommand>,
 }
 
 impl Queue {
@@ -46,7 +46,7 @@ impl Queue {
         max_concurrent_requests: usize,
     ) -> Self {
         // Create channels
-        let (queue_sender, queue_receiver) = mpsc::unbounded_channel();
+        let (queue_sender, queue_receiver) = mpsc::channel(max_concurrent_requests);
 
         // Launch background queue task
         std::thread::spawn(move || {
@@ -68,8 +68,8 @@ impl Queue {
         // Send append command to the background task managing the state
         // Unwrap is safe here
         self.queue_sender
-            .send(QueueCommand::Append(Box::new(entry), Span::current()))
-            .expect("Queue background task dropped the receiver. This is a bug.");
+            .try_send(QueueCommand::Append(Box::new(entry), Span::current()))
+            .expect("Queue background task dropped the receiver or the receiver is too behind. This is a bug.");
     }
 
     /// Get the next batch from the queue
@@ -80,11 +80,11 @@ impl Queue {
         // Send next batch command to the background task managing the state
         // Unwrap is safe here
         self.queue_sender
-            .send(QueueCommand::NextBatch {
+            .try_send(QueueCommand::NextBatch {
                 response_sender,
                 span: Span::current(),
             })
-            .expect("Queue background task dropped the receiver. This is a bug.");
+            .expect("Queue background task dropped the receiver or the receiver is too behind. This is a bug.");
         // Await on response channel
         // Unwrap is safe here
         response_receiver.await.expect(
@@ -99,7 +99,7 @@ fn queue_blocking_task(
     max_batch_tokens: usize,
     max_batch_requests: Option<usize>,
     max_concurrent_requests: usize,
-    mut queue_receiver: mpsc::UnboundedReceiver<QueueCommand>,
+    mut queue_receiver: mpsc::Receiver<QueueCommand>,
 ) {
     let capacity = max_batch_requests.unwrap_or(max_concurrent_requests);
 
@@ -110,7 +110,8 @@ fn queue_blocking_task(
             QueueCommand::Append(entry, span) => {
                 let _span = span.entered();
                 entries.push_back(*entry);
-                metrics::increment_gauge!("te_queue_size", 1.0);
+                let gauge = metrics::gauge!("te_queue_size");
+                gauge.increment(1.0);
             }
             QueueCommand::NextBatch {
                 response_sender,
@@ -137,7 +138,8 @@ fn queue_blocking_task(
                     // Filter entries where the response receiver was dropped (== entries where the request
                     // was dropped by the client)
                     if entry.metadata.response_tx.is_closed() {
-                        metrics::increment_counter!("te_request_failure", "err" => "dropped");
+                        let counter = metrics::counter!("te_request_failure", "err" => "dropped");
+                        counter.increment(1);
                         continue;
                     }
 
@@ -197,9 +199,12 @@ fn queue_blocking_task(
 
                 let _ = response_sender.send(next_batch);
 
-                metrics::histogram!("te_batch_next_size", batch_size as f64);
-                metrics::histogram!("te_batch_next_tokens", current_tokens as f64);
-                metrics::gauge!("te_queue_size", entries.len() as f64);
+                let histogram = metrics::histogram!("te_batch_next_size");
+                histogram.record(batch_size as f64);
+                let histogram = metrics::histogram!("te_batch_next_tokens");
+                histogram.record(current_tokens as f64);
+                let gauge = metrics::gauge!("te_queue_size");
+                gauge.set(entries.len() as f64)
             }
         }
     }
diff --git a/core/src/tokenization.rs b/core/src/tokenization.rs
index 07226823..71617daf 100644
--- a/core/src/tokenization.rs
+++ b/core/src/tokenization.rs
@@ -1,16 +1,19 @@
 /// Payload tokenization logic
 use crate::TextEmbeddingsError;
+use std::collections::HashMap;
 use tokenizers::tokenizer::Tokenizer;
 pub use tokenizers::Encoding as RawEncoding;
 use tokenizers::{TruncationDirection, TruncationParams, TruncationStrategy};
-use tokio::sync::{mpsc, oneshot};
+use tokio::sync::oneshot;
 use tracing::{instrument, Span};
 
+static MAX_CHAR_MULTIPLIER: usize = 250;
+
 /// Validation
 #[derive(Debug, Clone)]
 pub struct Tokenization {
     /// Channel to communicate with the background tokenization task
-    sender: mpsc::UnboundedSender<TokenizerRequest>,
+    sender: async_channel::Sender<TokenizerRequest>,
 }
 
 impl Tokenization {
@@ -19,43 +22,33 @@ impl Tokenization {
         tokenizer: Tokenizer,
         max_input_length: usize,
         position_offset: usize,
+        default_prompt: Option<String>,
+        prompts: Option<HashMap<String, String>>,
     ) -> Self {
         tracing::info!("Starting {workers} tokenization workers");
 
         // Create channel
-        let (sender, mut round_robin_receiver) = mpsc::unbounded_channel();
-        let mut senders = Vec::with_capacity(workers);
+        let (sender, receiver) = async_channel::bounded(workers * 4);
 
         // Create workers
         for _ in 0..workers {
             let tokenizer_clone = tokenizer.clone();
-            let (tokenizer_sender, tokenizer_receiver) = mpsc::unbounded_channel();
-            senders.push(tokenizer_sender);
-
+            let receiver_clone = receiver.clone();
+            let default_prompt_clone = default_prompt.clone();
+            let prompts_clone = prompts.clone();
             // Spawn worker
             std::thread::spawn(move || {
                 tokenizer_worker(
                     tokenizer_clone,
                     max_input_length,
                     position_offset,
-                    tokenizer_receiver,
+                    default_prompt_clone,
+                    prompts_clone,
+                    receiver_clone,
                 )
             });
         }
 
-        // Create tokenization round robin task
-        tokio::spawn(async move {
-            // Loop over requests
-            loop {
-                for sender in &senders {
-                    match round_robin_receiver.recv().await {
-                        None => return,
-                        Some(request) => sender.send(request).unwrap(),
-                    };
-                }
-            }
-        });
-
         Self { sender }
     }
 
@@ -65,6 +58,7 @@ impl Tokenization {
         inputs: EncodingInput,
         truncate: bool,
         truncation_direction: TruncationDirection,
+        prompt_name: Option<String>,
     ) -> Result<ValidEncoding, TextEmbeddingsError> {
         // Check if inputs is empty
         if inputs.is_empty() {
@@ -82,9 +76,11 @@ impl Tokenization {
                 inputs,
                 truncate,
                 truncation_direction,
+                prompt_name,
                 response_sender,
                 Span::current(),
             ))
+            .await
             .expect("Tokenization background task dropped the receiver. This is a bug.");
 
         // Await on response channel
@@ -97,7 +93,8 @@ impl Tokenization {
         &self,
         inputs: EncodingInput,
         add_special_tokens: bool,
-    ) -> Result<RawEncoding, TextEmbeddingsError> {
+        prompt_name: Option<String>,
+    ) -> Result<(Option<String>, RawEncoding), TextEmbeddingsError> {
         // Check if inputs is empty
         if inputs.is_empty() {
             return Err(TextEmbeddingsError::Validation(
@@ -113,9 +110,11 @@ impl Tokenization {
             .send(TokenizerRequest::Tokenize(
                 inputs,
                 add_special_tokens,
+                prompt_name,
                 response_sender,
                 Span::current(),
             ))
+            .await
             .expect("Tokenization background task dropped the receiver. This is a bug.");
 
         // Await on response channel
@@ -147,6 +146,7 @@ impl Tokenization {
                 response_sender,
                 Span::current(),
             ))
+            .await
             .expect("Tokenization background task dropped the receiver. This is a bug.");
 
         // Await on response channel
@@ -160,20 +160,28 @@ fn tokenizer_worker(
     mut tokenizer: Tokenizer,
     max_input_length: usize,
     position_offset: usize,
-    mut receiver: mpsc::UnboundedReceiver<TokenizerRequest>,
+    default_prompt: Option<String>,
+    prompts: Option<HashMap<String, String>>,
+    receiver: async_channel::Receiver<TokenizerRequest>,
 ) {
     // Loop over requests
-    while let Some(request) = receiver.blocking_recv() {
+    while let Ok(request) = receiver.recv_blocking() {
         match request {
             TokenizerRequest::Encode(
                 inputs,
                 truncate,
                 truncation_direction,
+                prompt_name,
                 response_tx,
                 parent_span,
             ) => {
                 parent_span.in_scope(|| {
                     if !response_tx.is_closed() {
+                        let default_prompt_clone = match prompt_name {
+                            None => default_prompt.clone(),
+                            Some(_) => None,
+                        };
+
                         // It's possible that the user dropped its request resulting in a send error.
                         // We just discard the error
                         let _ = response_tx.send(encode_input(
@@ -182,20 +190,38 @@ fn tokenizer_worker(
                             truncation_direction,
                             max_input_length,
                             position_offset,
+                            default_prompt_clone,
+                            prompt_name,
+                            prompts.as_ref(),
                             &mut tokenizer,
                         ));
                     }
                 })
             }
-            TokenizerRequest::Tokenize(inputs, add_special_tokens, response_tx, parent_span) => {
+            TokenizerRequest::Tokenize(
+                inputs,
+                add_special_tokens,
+                prompt_name,
+                response_tx,
+                parent_span,
+            ) => {
                 parent_span.in_scope(|| {
                     if !response_tx.is_closed() {
+                        let default_prompt_clone = match prompt_name {
+                            None => default_prompt.clone(),
+                            Some(_) => None,
+                        };
+
                         // It's possible that the user dropped its request resulting in a send error.
                         // We just discard the error
                         let _ = response_tx.send(tokenize_input(
                             inputs,
                             add_special_tokens,
+                            max_input_length,
                             None,
+                            default_prompt_clone,
+                            prompt_name,
+                            prompts.as_ref(),
                             &mut tokenizer,
                         ));
                     }
@@ -225,40 +251,117 @@ fn decode_ids(
         .decode(&ids, skip_special_tokens)?)
 }
 
+fn prepare_pre_prompt(
+    default_prompt: Option<String>,
+    prompt_name: Option<String>,
+    prompts: Option<&HashMap<String, String>>,
+) -> Result<Option<String>, TextEmbeddingsError> {
+    let pre_prompt = if let Some(prompt_name) = prompt_name.as_ref() {
+        match prompts {
+            None => {
+                return Err(TextEmbeddingsError::Validation(format!("`default-prompt-name` is set to `{prompt_name}` but no prompts were found in the Sentence Transformers configuration")));
+            }
+            Some(prompts) if !prompts.contains_key(prompt_name) => {
+                return Err(TextEmbeddingsError::Validation(format!("`default-prompt-name` is set to `{prompt_name}` but it was not found in the Sentence Transformers prompts. Available prompts: {:?}", prompts.keys())));
+            }
+            Some(prompts) => prompts.get(prompt_name).cloned(),
+        }
+    } else {
+        default_prompt
+    };
+    Ok(pre_prompt)
+}
+
+#[allow(clippy::too_many_arguments)]
 fn tokenize_input(
-    inputs: EncodingInput,
+    mut inputs: EncodingInput,
     add_special_tokens: bool,
+    max_input_length: usize,
     truncate_params: Option<TruncationParams>,
+    default_prompt: Option<String>,
+    prompt_name: Option<String>,
+    prompts: Option<&HashMap<String, String>>,
     tokenizer: &mut Tokenizer,
-) -> Result<RawEncoding, TextEmbeddingsError> {
+) -> Result<(Option<String>, RawEncoding), TextEmbeddingsError> {
+    let pre_prompt = prepare_pre_prompt(default_prompt, prompt_name, prompts)?;
+
+    let input_chars = inputs.count_chars();
+    let limit = max_input_length * MAX_CHAR_MULTIPLIER;
+    if input_chars > limit {
+        if truncate_params.is_none() {
+            return Err(TextEmbeddingsError::Validation(format!(
+                "`inputs` must have less than {limit} characters. Given: {input_chars}"
+            )));
+        }
+        inputs.apply_limit(limit);
+    }
+
     let encoding = match inputs {
         // encode input
-        EncodingInput::Single(s) => tokenizer
-            .with_truncation(truncate_params)?
-            .encode::<String>(s, add_special_tokens)?,
-        EncodingInput::Dual(s1, s2) => {
-            tokenizer
+        EncodingInput::Single(s) => {
+            let s = if let Some(mut pre_prompt) = pre_prompt {
+                pre_prompt.push_str(&s);
+                pre_prompt
+            } else {
+                s
+            };
+
+            let encoding = tokenizer
                 .with_truncation(truncate_params)?
-                .encode::<(String, String)>((s1, s2), add_special_tokens)?
+                .encode::<&str>(&s, add_special_tokens)?;
+
+            (Some(s), encoding)
+        }
+        EncodingInput::Dual(s1, s2) => {
+            if pre_prompt.is_some() {
+                return Err(TextEmbeddingsError::Validation(
+                    "`prompt_name` cannot be set with dual inputs".to_string(),
+                ));
+            }
+
+            (
+                None,
+                tokenizer
+                    .with_truncation(truncate_params)?
+                    .encode::<(String, String)>((s1, s2), add_special_tokens)?,
+            )
         }
         // input is encoded -> convert to tokenizers Encoding
         EncodingInput::Ids(ids) => {
-            let text = tokenizer.decode(&ids, false)?;
-            tokenizer
-                .with_truncation(truncate_params)?
-                .encode::<String>(text, false)?
+            if let Some(mut pre_prompt) = pre_prompt {
+                let text = tokenizer.decode(&ids, true)?;
+                pre_prompt.push_str(&text);
+
+                let encoding = tokenizer
+                    .with_truncation(truncate_params)?
+                    .encode::<&str>(&pre_prompt, true)?;
+
+                (Some(pre_prompt), encoding)
+            } else {
+                let text = tokenizer.decode(&ids, false)?;
+
+                let encoding = tokenizer
+                    .with_truncation(truncate_params)?
+                    .encode::<&str>(&text, false)?;
+
+                (Some(text), encoding)
+            }
         }
     };
     Ok(encoding)
 }
 
 /// Get input length and optionally truncate it
+#[allow(clippy::too_many_arguments)]
 fn encode_input(
     inputs: EncodingInput,
     truncate: bool,
     truncation_direction: TruncationDirection,
     max_input_length: usize,
     position_offset: usize,
+    default_prompt: Option<String>,
+    prompt_name: Option<String>,
+    prompts: Option<&HashMap<String, String>>,
     tokenizer: &mut Tokenizer,
 ) -> Result<ValidEncoding, TextEmbeddingsError> {
     // Default truncation params
@@ -269,7 +372,16 @@ fn encode_input(
         stride: 0,
     });
 
-    let encoding = tokenize_input(inputs, true, truncate_params, tokenizer)?;
+    let (_, encoding) = tokenize_input(
+        inputs,
+        true,
+        max_input_length,
+        truncate_params,
+        default_prompt,
+        prompt_name,
+        prompts,
+        tokenizer,
+    )?;
     let seq_len = encoding.len();
 
     if seq_len > max_input_length {
@@ -277,7 +389,8 @@ fn encode_input(
             "`inputs` must have less than {max_input_length} tokens. Given: {seq_len}"
         )));
     }
-    metrics::histogram!("te_request_input_length", seq_len as f64);
+    let histogram = metrics::histogram!("te_request_input_length");
+    histogram.record(seq_len as f64);
     Ok(ValidEncoding {
         input_ids: encoding.get_ids().to_vec(),
         token_type_ids: encoding.get_type_ids().to_vec(),
@@ -308,6 +421,33 @@ impl EncodingInput {
             EncodingInput::Ids(v) => v.is_empty(),
         }
     }
+
+    fn count_chars(&self) -> usize {
+        match self {
+            EncodingInput::Single(s) => s.chars().count(),
+            EncodingInput::Dual(s1, s2) => s1.chars().count() + s2.chars().count(),
+            EncodingInput::Ids(v) => v.len(),
+        }
+    }
+
+    fn apply_limit(&mut self, limit: usize) {
+        let truncate_string = |s: &mut String, limit: usize| {
+            if s.is_char_boundary(limit) {
+                s.truncate(limit)
+            }
+        };
+
+        match self {
+            EncodingInput::Single(s) => {
+                truncate_string(s, limit);
+            }
+            EncodingInput::Dual(s1, s2) => {
+                truncate_string(s1, limit / 2);
+                truncate_string(s2, limit / 2);
+            }
+            EncodingInput::Ids(_) => {}
+        }
+    }
 }
 
 impl From<String> for EncodingInput {
@@ -327,13 +467,15 @@ enum TokenizerRequest {
         EncodingInput,
         bool,
         TruncationDirection,
+        Option<String>,
         oneshot::Sender<Result<ValidEncoding, TextEmbeddingsError>>,
         Span,
     ),
     Tokenize(
         EncodingInput,
         bool,
-        oneshot::Sender<Result<RawEncoding, TextEmbeddingsError>>,
+        Option<String>,
+        oneshot::Sender<Result<(Option<String>, RawEncoding), TextEmbeddingsError>>,
         Span,
     ),
     Decode(
diff --git a/docs/openapi.json b/docs/openapi.json
index 7368145e..f12811d3 100644
--- a/docs/openapi.json
+++ b/docs/openapi.json
@@ -10,7 +10,7 @@
       "name": "Apache 2.0",
       "url": "https://www.apache.org/licenses/LICENSE-2.0"
     },
-    "version": "1.2.3"
+    "version": "1.5.1"
   },
   "paths": {
     "/decode": {
@@ -19,7 +19,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Decode input ids",
-        "description": "Decode input ids",
         "operationId": "decode",
         "requestBody": {
           "content": {
@@ -65,7 +64,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Get Embeddings. Returns a 424 status code if the model is not an embedding model.",
-        "description": "Get Embeddings. Returns a 424 status code if the model is not an embedding model.",
         "operationId": "embed",
         "requestBody": {
           "content": {
@@ -153,7 +151,7 @@
           "Text Embeddings Inference"
         ],
         "summary": "Get all Embeddings without Pooling.",
-        "description": "Get all Embeddings without Pooling.\nReturns a 424 status code if the model is not an embedding model.",
+        "description": "Returns a 424 status code if the model is not an embedding model.",
         "operationId": "embed_all",
         "requestBody": {
           "content": {
@@ -241,7 +239,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Get Sparse Embeddings. Returns a 424 status code if the model is not an embedding model with SPLADE pooling.",
-        "description": "Get Sparse Embeddings. Returns a 424 status code if the model is not an embedding model with SPLADE pooling.",
         "operationId": "embed_sparse",
         "requestBody": {
           "content": {
@@ -323,101 +320,12 @@
         }
       }
     },
-    "/embeddings": {
-      "post": {
-        "tags": [
-          "Text Embeddings Inference"
-        ],
-        "summary": "OpenAI compatible route. Returns a 424 status code if the model is not an embedding model.",
-        "description": "OpenAI compatible route. Returns a 424 status code if the model is not an embedding model.",
-        "operationId": "openai_embed",
-        "requestBody": {
-          "content": {
-            "application/json": {
-              "schema": {
-                "$ref": "#/components/schemas/OpenAICompatRequest"
-              }
-            }
-          },
-          "required": true
-        },
-        "responses": {
-          "200": {
-            "description": "Embeddings",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/OpenAICompatResponse"
-                }
-              }
-            }
-          },
-          "413": {
-            "description": "Batch size error",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
-                },
-                "example": {
-                  "message": "Batch size error",
-                  "type": "validation"
-                }
-              }
-            }
-          },
-          "422": {
-            "description": "Tokenization error",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
-                },
-                "example": {
-                  "message": "Tokenization error",
-                  "type": "tokenizer"
-                }
-              }
-            }
-          },
-          "424": {
-            "description": "Embedding Error",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
-                },
-                "example": {
-                  "message": "Inference failed",
-                  "type": "backend"
-                }
-              }
-            }
-          },
-          "429": {
-            "description": "Model is overloaded",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
-                },
-                "example": {
-                  "message": "Model is overloaded",
-                  "type": "overloaded"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
     "/health": {
       "get": {
         "tags": [
           "Text Embeddings Inference"
         ],
         "summary": "Health check method",
-        "description": "Health check method",
         "operationId": "health",
         "responses": {
           "200": {
@@ -446,7 +354,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Text Embeddings Inference endpoint info",
-        "description": "Text Embeddings Inference endpoint info",
         "operationId": "get_model_info",
         "responses": {
           "200": {
@@ -468,7 +375,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Prometheus metrics scrape endpoint",
-        "description": "Prometheus metrics scrape endpoint",
         "operationId": "metrics",
         "responses": {
           "200": {
@@ -490,7 +396,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Get Predictions. Returns a 424 status code if the model is not a Sequence Classification model",
-        "description": "Get Predictions. Returns a 424 status code if the model is not a Sequence Classification model",
         "operationId": "predict",
         "requestBody": {
           "content": {
@@ -578,7 +483,7 @@
           "Text Embeddings Inference"
         ],
         "summary": "Get Ranks. Returns a 424 status code if the model is not a Sequence Classification model with",
-        "description": "Get Ranks. Returns a 424 status code if the model is not a Sequence Classification model with\na single class.",
+        "description": "a single class.",
         "operationId": "rerank",
         "requestBody": {
           "content": {
@@ -660,13 +565,99 @@
         }
       }
     },
+    "/similarity": {
+      "post": {
+        "tags": [
+          "Text Embeddings Inference"
+        ],
+        "summary": "Get Sentence Similarity. Returns a 424 status code if the model is not an embedding model.",
+        "operationId": "similarity",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/SimilarityRequest"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "Sentence Similarity",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/SimilarityResponse"
+                }
+              }
+            }
+          },
+          "413": {
+            "description": "Batch size error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Batch size error",
+                  "error_type": "validation"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Tokenization error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Tokenization error",
+                  "error_type": "tokenizer"
+                }
+              }
+            }
+          },
+          "424": {
+            "description": "Embedding Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Inference failed",
+                  "error_type": "backend"
+                }
+              }
+            }
+          },
+          "429": {
+            "description": "Model is overloaded",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Model is overloaded",
+                  "error_type": "overloaded"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
     "/tokenize": {
       "post": {
         "tags": [
           "Text Embeddings Inference"
         ],
         "summary": "Tokenize inputs",
-        "description": "Tokenize inputs",
         "operationId": "tokenize",
         "requestBody": {
           "content": {
@@ -706,19 +697,18 @@
         }
       }
     },
-    "/vertex": {
+    "/v1/embeddings": {
       "post": {
         "tags": [
           "Text Embeddings Inference"
         ],
-        "summary": "Generate embeddings from a Vertex request",
-        "description": "Generate embeddings from a Vertex request",
-        "operationId": "vertex_compatibility",
+        "summary": "OpenAI compatible route. Returns a 424 status code if the model is not an embedding model.",
+        "operationId": "openai_embed",
         "requestBody": {
           "content": {
             "application/json": {
               "schema": {
-                "$ref": "#/components/schemas/VertexRequest"
+                "$ref": "#/components/schemas/OpenAICompatRequest"
               }
             }
           },
@@ -726,18 +716,25 @@
         },
         "responses": {
           "200": {
-            "description": "Results"
+            "description": "Embeddings",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/OpenAICompatResponse"
+                }
+              }
+            }
           },
           "413": {
             "description": "Batch size error",
             "content": {
               "application/json": {
                 "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
+                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
                 },
                 "example": {
-                  "error": "Batch size error",
-                  "error_type": "validation"
+                  "message": "Batch size error",
+                  "type": "validation"
                 }
               }
             }
@@ -747,25 +744,25 @@
             "content": {
               "application/json": {
                 "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
+                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
                 },
                 "example": {
-                  "error": "Tokenization error",
-                  "error_type": "tokenizer"
+                  "message": "Tokenization error",
+                  "type": "tokenizer"
                 }
               }
             }
           },
           "424": {
-            "description": "Error",
+            "description": "Embedding Error",
             "content": {
               "application/json": {
                 "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
+                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
                 },
                 "example": {
-                  "error": "Inference failed",
-                  "error_type": "backend"
+                  "message": "Inference failed",
+                  "type": "backend"
                 }
               }
             }
@@ -775,11 +772,11 @@
             "content": {
               "application/json": {
                 "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
+                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
                 },
                 "example": {
-                  "error": "Model is overloaded",
-                  "error_type": "overloaded"
+                  "message": "Model is overloaded",
+                  "type": "overloaded"
                 }
               }
             }
@@ -852,10 +849,26 @@
           "inputs": {
             "$ref": "#/components/schemas/Input"
           },
+          "prompt_name": {
+            "type": "string",
+            "description": "The name of the prompt that should be used by for encoding. If not set, no prompt\nwill be applied.\n\nMust be a key in the `sentence-transformers` configuration `prompts` dictionary.\n\nFor example if ``prompt_name`` is \"query\" and the ``prompts`` is {\"query\": \"query: \", ...},\nthen the sentence \"What is the capital of France?\" will be encoded as\n\"query: What is the capital of France?\" because the prompt text will be prepended before\nany text to encode.",
+            "default": "null",
+            "example": "null",
+            "nullable": true
+          },
           "truncate": {
             "type": "boolean",
             "default": "false",
-            "example": "false"
+            "example": "false",
+            "nullable": true
+          },
+          "truncation_direction": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/TruncationDirection"
+              }
+            ],
+            "default": "right"
           }
         }
       },
@@ -895,10 +908,26 @@
             "default": "true",
             "example": "true"
           },
+          "prompt_name": {
+            "type": "string",
+            "description": "The name of the prompt that should be used by for encoding. If not set, no prompt\nwill be applied.\n\nMust be a key in the `sentence-transformers` configuration `prompts` dictionary.\n\nFor example if ``prompt_name`` is \"query\" and the ``prompts`` is {\"query\": \"query: \", ...},\nthen the sentence \"What is the capital of France?\" will be encoded as\n\"query: What is the capital of France?\" because the prompt text will be prepended before\nany text to encode.",
+            "default": "null",
+            "example": "null",
+            "nullable": true
+          },
           "truncate": {
             "type": "boolean",
             "default": "false",
-            "example": "false"
+            "example": "false",
+            "nullable": true
+          },
+          "truncation_direction": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/TruncationDirection"
+              }
+            ],
+            "default": "right"
           }
         }
       },
@@ -928,10 +957,26 @@
           "inputs": {
             "$ref": "#/components/schemas/Input"
           },
+          "prompt_name": {
+            "type": "string",
+            "description": "The name of the prompt that should be used by for encoding. If not set, no prompt\nwill be applied.\n\nMust be a key in the `sentence-transformers` configuration `prompts` dictionary.\n\nFor example if ``prompt_name`` is \"query\" and the ``prompts`` is {\"query\": \"query: \", ...},\nthen the sentence \"What is the capital of France?\" will be encoded as\n\"query: What is the capital of France?\" because the prompt text will be prepended before\nany text to encode.",
+            "default": "null",
+            "example": "null",
+            "nullable": true
+          },
           "truncate": {
             "type": "boolean",
             "default": "false",
-            "example": "false"
+            "example": "false",
+            "nullable": true
+          },
+          "truncation_direction": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/TruncationDirection"
+              }
+            ],
+            "default": "right"
           }
         }
       },
@@ -944,6 +989,20 @@
           }
         }
       },
+      "Embedding": {
+        "oneOf": [
+          {
+            "type": "array",
+            "items": {
+              "type": "number",
+              "format": "float"
+            }
+          },
+          {
+            "type": "string"
+          }
+        ]
+      },
       "EmbeddingModel": {
         "type": "object",
         "required": [
@@ -956,6 +1015,13 @@
           }
         }
       },
+      "EncodingFormat": {
+        "type": "string",
+        "enum": [
+          "float",
+          "base64"
+        ]
+      },
       "ErrorResponse": {
         "type": "object",
         "required": [
@@ -991,10 +1057,14 @@
           "max_input_length",
           "max_batch_tokens",
           "max_client_batch_size",
+          "auto_truncate",
           "tokenization_workers",
           "version"
         ],
         "properties": {
+          "auto_truncate": {
+            "type": "boolean"
+          },
           "docker_label": {
             "type": "string",
             "example": "null",
@@ -1065,12 +1135,12 @@
       "Input": {
         "oneOf": [
           {
-            "type": "string"
+            "$ref": "#/components/schemas/InputType"
           },
           {
             "type": "array",
             "items": {
-              "type": "string"
+              "$ref": "#/components/schemas/InputType"
             }
           }
         ]
@@ -1098,6 +1168,21 @@
           }
         ]
       },
+      "InputType": {
+        "oneOf": [
+          {
+            "type": "string"
+          },
+          {
+            "type": "array",
+            "items": {
+              "type": "integer",
+              "format": "int32",
+              "minimum": 0
+            }
+          }
+        ]
+      },
       "ModelType": {
         "oneOf": [
           {
@@ -1144,16 +1229,7 @@
         ],
         "properties": {
           "embedding": {
-            "type": "array",
-            "items": {
-              "type": "number",
-              "format": "float"
-            },
-            "example": [
-              0.0,
-              1.0,
-              2.0
-            ]
+            "$ref": "#/components/schemas/Embedding"
           },
           "index": {
             "type": "integer",
@@ -1193,6 +1269,14 @@
           "input"
         ],
         "properties": {
+          "encoding_format": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/EncodingFormat"
+              }
+            ],
+            "default": "float"
+          },
           "input": {
             "$ref": "#/components/schemas/Input"
           },
@@ -1317,7 +1401,16 @@
           "truncate": {
             "type": "boolean",
             "default": "false",
-            "example": "false"
+            "example": "false",
+            "nullable": true
+          },
+          "truncation_direction": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/TruncationDirection"
+              }
+            ],
+            "default": "right"
           }
         }
       },
@@ -1416,7 +1509,16 @@
           "truncate": {
             "type": "boolean",
             "default": "false",
-            "example": "false"
+            "example": "false",
+            "nullable": true
+          },
+          "truncation_direction": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/TruncationDirection"
+              }
+            ],
+            "default": "right"
           }
         }
       },
@@ -1426,6 +1528,91 @@
           "$ref": "#/components/schemas/Rank"
         }
       },
+      "SimilarityInput": {
+        "type": "object",
+        "required": [
+          "source_sentence",
+          "sentences"
+        ],
+        "properties": {
+          "sentences": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            },
+            "description": "A list of strings which will be compared against the source_sentence.",
+            "example": [
+              "What is Machine Learning?"
+            ]
+          },
+          "source_sentence": {
+            "type": "string",
+            "description": "The string that you wish to compare the other strings with. This can be a phrase, sentence,\nor longer passage, depending on the model being used.",
+            "example": "What is Deep Learning?"
+          }
+        }
+      },
+      "SimilarityParameters": {
+        "type": "object",
+        "required": [
+          "truncation_direction"
+        ],
+        "properties": {
+          "prompt_name": {
+            "type": "string",
+            "description": "The name of the prompt that should be used by for encoding. If not set, no prompt\nwill be applied.\n\nMust be a key in the `sentence-transformers` configuration `prompts` dictionary.\n\nFor example if ``prompt_name`` is \"query\" and the ``prompts`` is {\"query\": \"query: \", ...},\nthen the sentence \"What is the capital of France?\" will be encoded as\n\"query: What is the capital of France?\" because the prompt text will be prepended before\nany text to encode.",
+            "default": "null",
+            "example": "null",
+            "nullable": true
+          },
+          "truncate": {
+            "type": "boolean",
+            "default": "false",
+            "example": "false",
+            "nullable": true
+          },
+          "truncation_direction": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/TruncationDirection"
+              }
+            ],
+            "default": "right"
+          }
+        }
+      },
+      "SimilarityRequest": {
+        "type": "object",
+        "required": [
+          "inputs"
+        ],
+        "properties": {
+          "inputs": {
+            "$ref": "#/components/schemas/SimilarityInput"
+          },
+          "parameters": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/SimilarityParameters"
+              }
+            ],
+            "default": "null",
+            "nullable": true
+          }
+        }
+      },
+      "SimilarityResponse": {
+        "type": "array",
+        "items": {
+          "type": "number",
+          "format": "float"
+        },
+        "example": [
+          0.0,
+          1.0,
+          0.5
+        ]
+      },
       "SimpleToken": {
         "type": "object",
         "required": [
@@ -1479,6 +1666,19 @@
           }
         }
       },
+      "TokenizeInput": {
+        "oneOf": [
+          {
+            "type": "string"
+          },
+          {
+            "type": "array",
+            "items": {
+              "type": "string"
+            }
+          }
+        ]
+      },
       "TokenizeRequest": {
         "type": "object",
         "required": [
@@ -1491,7 +1691,14 @@
             "example": "true"
           },
           "inputs": {
-            "$ref": "#/components/schemas/Input"
+            "$ref": "#/components/schemas/TokenizeInput"
+          },
+          "prompt_name": {
+            "type": "string",
+            "description": "The name of the prompt that should be used by for encoding. If not set, no prompt\nwill be applied.\n\nMust be a key in the `sentence-transformers` configuration `prompts` dictionary.\n\nFor example if ``prompt_name`` is \"query\" and the ``prompts`` is {\"query\": \"query: \", ...},\nthen the sentence \"What is the capital of France?\" will be encoded as\n\"query: What is the capital of France?\" because the prompt text will be prepended before\nany text to encode.",
+            "default": "null",
+            "example": "null",
+            "nullable": true
           }
         }
       },
@@ -1515,273 +1722,12 @@
           ]
         ]
       },
-      "VertexInstance": {
-        "oneOf": [
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/EmbedRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "embed"
-                    ]
-                  }
-                }
-              }
-            ]
-          },
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/EmbedAllRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "embed_all"
-                    ]
-                  }
-                }
-              }
-            ]
-          },
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/EmbedSparseRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "embed_sparse"
-                    ]
-                  }
-                }
-              }
-            ]
-          },
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/PredictRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "predict"
-                    ]
-                  }
-                }
-              }
-            ]
-          },
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/RerankRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "rerank"
-                    ]
-                  }
-                }
-              }
-            ]
-          },
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/TokenizeRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "tokenize"
-                    ]
-                  }
-                }
-              }
-            ]
-          }
-        ],
-        "discriminator": {
-          "propertyName": "type"
-        }
-      },
-      "VertexRequest": {
-        "type": "object",
-        "required": [
-          "instances"
-        ],
-        "properties": {
-          "instances": {
-            "type": "array",
-            "items": {
-              "$ref": "#/components/schemas/VertexInstance"
-            }
-          }
-        }
-      },
-      "VertexResponse": {
-        "type": "array",
-        "items": {
-          "$ref": "#/components/schemas/VertexResponseInstance"
-        }
-      },
-      "VertexResponseInstance": {
-        "oneOf": [
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/EmbedResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "embed"
-                ]
-              }
-            }
-          },
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/EmbedAllResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "embed_all"
-                ]
-              }
-            }
-          },
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/EmbedSparseResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "embed_sparse"
-                ]
-              }
-            }
-          },
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/PredictResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "predict"
-                ]
-              }
-            }
-          },
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/RerankResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "rerank"
-                ]
-              }
-            }
-          },
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/TokenizeResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "tokenize"
-                ]
-              }
-            }
-          }
-        ],
-        "discriminator": {
-          "propertyName": "type"
-        }
+      "TruncationDirection": {
+        "type": "string",
+        "enum": [
+          "Left",
+          "Right"
+        ]
       }
     }
   },
diff --git a/docs/source/en/cli_arguments.md b/docs/source/en/cli_arguments.md
index 5efa63cf..e25e60b7 100644
--- a/docs/source/en/cli_arguments.md
+++ b/docs/source/en/cli_arguments.md
@@ -62,9 +62,11 @@ Options:
           [env: POOLING=]
 
           Possible values:
-          - cls:    Select the CLS token as embedding
-          - mean:   Apply Mean pooling to the model embeddings
-          - splade: Apply SPLADE (Sparse Lexical and Expansion) to the model embeddings. This option is only available if the loaded model is a `ForMaskedLM` Transformer model
+          - cls:        Select the CLS token as embedding
+          - mean:       Apply Mean pooling to the model embeddings
+          - splade:     Apply SPLADE (Sparse Lexical and Expansion) to the model embeddings. This option is only
+          available if the loaded model is a `ForMaskedLM` Transformer model
+          - last-token: Select the last token as embedding
 
       --max-concurrent-requests <MAX_CONCURRENT_REQUESTS>
           The maximum amount of concurrent requests for this particular deployment.
@@ -98,6 +100,37 @@ Options:
           [env: MAX_CLIENT_BATCH_SIZE=]
           [default: 32]
 
+      --auto-truncate
+          Automatically truncate inputs that are longer than the maximum supported size
+
+          Unused for gRPC servers
+
+          [env: AUTO_TRUNCATE=]
+
+      --default-prompt-name <DEFAULT_PROMPT_NAME>
+          The name of the prompt that should be used by default for encoding. If not set, no prompt will be applied.
+
+          Must be a key in the `sentence-transformers` configuration `prompts` dictionary.
+
+          For example if ``default_prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...}, then the
+          sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" because
+          the prompt text will be prepended before any text to encode.
+
+          The argument '--default-prompt-name <DEFAULT_PROMPT_NAME>' cannot be used with '--default-prompt <DEFAULT_PROMPT>`
+
+          [env: DEFAULT_PROMPT_NAME=]
+
+      --default-prompt <DEFAULT_PROMPT>
+          The prompt that should be used by default for encoding. If not set, no prompt will be applied.
+
+          For example if ``default_prompt`` is "query: " then the sentence "What is the capital of France?" will be
+          encoded as "query: What is the capital of France?" because the prompt text will be prepended before any text
+          to encode.
+
+          The argument '--default-prompt <DEFAULT_PROMPT>' cannot be used with '--default-prompt-name <DEFAULT_PROMPT_NAME>`
+
+          [env: DEFAULT_PROMPT=]
+
       --hf-api-token <HF_API_TOKEN>
           Your HuggingFace hub token
 
@@ -126,7 +159,7 @@ Options:
           The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk
           for instance
 
-          [env: HUGGINGFACE_HUB_CACHE=/data]
+          [env: HUGGINGFACE_HUB_CACHE=]
 
       --payload-limit <PAYLOAD_LIMIT>
           Payload size limit in bytes
@@ -139,7 +172,8 @@ Options:
       --api-key <API_KEY>
           Set an api key for request authorization.
 
-          By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token.
+          By default the server responds to every request. With an api key set, the requests must have the Authorization
+          header set with the api key as Bearer token.
 
           [env: API_KEY=]
 
@@ -154,11 +188,13 @@ Options:
           [env: OTLP_ENDPOINT=]
 
       --otlp-service-name <OTLP_SERVICE_NAME>
-          The service name for opentelemetry.
+          The service name for opentelemetry. e.g. `text-embeddings-inference.server`
 
           [env: OTLP_SERVICE_NAME=]
           [default: text-embeddings-inference.server]
 
       --cors-allow-origin <CORS_ALLOW_ORIGIN>
+          Unused for gRPC servers
+
           [env: CORS_ALLOW_ORIGIN=]
 ```
diff --git a/docs/source/en/private_models.md b/docs/source/en/private_models.md
index bd9041c7..d11d66f3 100644
--- a/docs/source/en/private_models.md
+++ b/docs/source/en/private_models.md
@@ -24,10 +24,10 @@ Once you have confirmed that you have access to the model:
 - Navigate to your account's [Profile | Settings | Access Tokens page](https://huggingface.co/settings/tokens).
 - Generate and copy a read token.
 
-If you're the CLI, set the `HUGGING_FACE_HUB_TOKEN` environment variable. For example:
+If you're the CLI, set the `HF_API_TOKEN` environment variable. For example:
 
 ```shell
-export HUGGING_FACE_HUB_TOKEN=<YOUR READ TOKEN>
+export HF_API_TOKEN=<YOUR READ TOKEN>
 ```
 
 Alternatively, you can provide the token when deploying the model with Docker:
@@ -37,5 +37,5 @@ model=<your private model>
 volume=$PWD/data
 token=<your cli Hugging Face Hub token>
 
-docker run --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model
+docker run --gpus all -e HF_API_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5 --model-id $model
 ```
diff --git a/docs/source/en/quick_tour.md b/docs/source/en/quick_tour.md
index c0fe008c..aceb5674 100644
--- a/docs/source/en/quick_tour.md
+++ b/docs/source/en/quick_tour.md
@@ -31,15 +31,13 @@ Finally, deploy your model. Let's say you want to use `BAAI/bge-large-en-v1.5`.
 
 ```shell
 model=BAAI/bge-large-en-v1.5
-revision=refs/pr/5
 volume=$PWD/data
 
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model --revision $revision
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5 --model-id $model
 ```
 
 <Tip>
 
-Here we pass a `revision=refs/pr/5` because the `safetensors` variant of this model is currently in a pull request.
 We also recommend sharing a volume with the Docker container (`volume=$PWD/data`) to avoid downloading weights every run.
 
 </Tip>
@@ -66,10 +64,9 @@ Let's say you want to use `BAAI/bge-reranker-large`:
 
 ```shell
 model=BAAI/bge-reranker-large
-revision=refs/pr/4
 volume=$PWD/data
 
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model --revision $revision
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5 --model-id $model
 ```
 
 Once you have deployed a model, you can use the `rerank` endpoint to rank the similarity between a query and a list
@@ -90,7 +87,7 @@ You can also use classic Sequence Classification models like `SamLowe/roberta-ba
 model=SamLowe/roberta-base-go_emotions
 volume=$PWD/data
 
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5 --model-id $model
 ```
 
 Once you have deployed the model you can use the `predict` endpoint to get the emotions most associated with an input:
@@ -121,3 +118,26 @@ curl 127.0.0.1:8080/predict \
     -d '{"inputs":[["I like you."], ["I hate pineapples"]]}' \
     -H 'Content-Type: application/json'
 ```
+
+## Air gapped deployment
+
+To deploy Text Embeddings Inference in an air-gapped environment, first download the weights and then mount them inside
+the container using a volume.
+
+For example:
+
+```shell
+# (Optional) create a `models` directory
+mkdir models
+cd models
+
+# Make sure you have git-lfs installed (https://git-lfs.com)
+git lfs install
+git clone https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5
+
+# Set the models directory as the volume path
+volume=$PWD
+
+# Mount the models directory inside the container with a volume and set the model ID
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.5 --model-id /data/gte-base-en-v1.5
+```
diff --git a/docs/source/en/supported_models.md b/docs/source/en/supported_models.md
index 876e5d06..c5e1be85 100644
--- a/docs/source/en/supported_models.md
+++ b/docs/source/en/supported_models.md
@@ -20,19 +20,21 @@ We are continually expanding our support for other model types and plan to inclu
 
 ## Supported embeddings models
 
-Text Embeddings Inference currently supports BERT, CamemBERT, XLM-RoBERTa models with absolute positions and JinaBERT
-model with Alibi positions.
+Text Embeddings Inference currently supports Nomic, BERT, CamemBERT, XLM-RoBERTa models with absolute positions, JinaBERT
+model with Alibi positions and Mistral, Alibaba GTE and Qwen2 models with Rope positions.
 
 Below are some examples of the currently supported models:
 
-| MTEB Rank | Model Type   | Model ID                                                                                         |
-|-----------|--------------|--------------------------------------------------------------------------------------------------|
-| 6         | Bert         | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1)                                   |
-| 10        | XLM-RoBERTa  | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
-| N/A       | NomicBert    | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1)                       |
-| N/A       | NomicBert    | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5)                   |
-| N/A       | JinaBERT     | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en)             |
-| N/A       | JinaBERT     | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jniaai/jina-embeddings-v2-base-code)         |
+| MTEB Rank | Model Size     | Model Type  | Model ID                                                                                         |
+|-----------|----------------|-------------|--------------------------------------------------------------------------------------------------|
+| 1         | 7B (Very Slow) | Mistral     | [Salesforce/SFR-Embedding-2_R](https://hf.co/Salesforce/SFR-Embedding-2_R)                       |
+| 15        | 0.4B           | Alibaba GTE | [Alibaba-NLP/gte-large-en-v1.5](Alibaba-NLP/gte-large-en-v1.5)                                   |
+| 20        | 0.3B           | Bert        | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1)                                   |
+| 24        | 0.5B           | XLM-RoBERTa | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
+| N/A       | 0.1B           | NomicBert   | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1)                       |
+| N/A       | 0.1B           | NomicBert   | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5)                   |
+| N/A       | 0.1B           | JinaBERT    | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en)             |
+| N/A       | 0.1B           | JinaBERT    | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jinaai/jina-embeddings-v2-base-code)         |
 
 
 To explore the list of best performing text embeddings models, visit the
@@ -64,13 +66,13 @@ Find the appropriate Docker image for your hardware in the following table:
 
 | Architecture                        | Image                                                                    |
 |-------------------------------------|--------------------------------------------------------------------------|
-| CPU                                 | ghcr.io/huggingface/text-embeddings-inference:cpu-1.2                    |
+| CPU                                 | ghcr.io/huggingface/text-embeddings-inference:cpu-1.5                    |
 | Volta                               | NOT SUPPORTED                                                            |
-| Turing (T4, RTX 2000 series, ...)   | ghcr.io/huggingface/text-embeddings-inference:turing-1.2 (experimental)  |
-| Ampere 80 (A100, A30)               | ghcr.io/huggingface/text-embeddings-inference:1.2                        |
-| Ampere 86 (A10, A40, ...)           | ghcr.io/huggingface/text-embeddings-inference:86-1.2                     |
-| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-1.2                     |
-| Hopper (H100)                       | ghcr.io/huggingface/text-embeddings-inference:hopper-1.2 (experimental)  |
+| Turing (T4, RTX 2000 series, ...)   | ghcr.io/huggingface/text-embeddings-inference:turing-1.5 (experimental)  |
+| Ampere 80 (A100, A30)               | ghcr.io/huggingface/text-embeddings-inference:1.5                        |
+| Ampere 86 (A10, A40, ...)           | ghcr.io/huggingface/text-embeddings-inference:86-1.5                     |
+| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-1.5                     |
+| Hopper (H100)                       | ghcr.io/huggingface/text-embeddings-inference:hopper-1.5 (experimental)  |
 
 **Warning**: Flash Attention is turned off by default for the Turing image as it suffers from precision issues.
 You can turn Flash Attention v1 ON by using the `USE_FLASH_ATTENTION=True` environment variable.
diff --git a/load_tests/load.js b/load_tests/load.js
index b7e4524e..867f9fdb 100644
--- a/load_tests/load.js
+++ b/load_tests/load.js
@@ -2,7 +2,7 @@ import {check} from 'k6';
 import http from 'k6/http';
 import {Trend} from 'k6/metrics';
 
-const host = __ENV.HOST || '127.0.0.1:8080';
+const host = __ENV.HOST || '127.0.0.1:3000';
 
 const totalTime = new Trend('total_time', true);
 const tokenizationTIme = new Trend('tokenization_time', true);
@@ -27,7 +27,7 @@ export const options = {
             executor: 'constant-arrival-rate',
             duration: '30s',
             preAllocatedVUs: 5000,
-            rate: 1000,
+            rate: 50,
             timeUnit: '1s',
             gracefulStop: '1s',
         },
@@ -36,8 +36,9 @@ export const options = {
 
 export default function () {
     const payload = JSON.stringify({
-        query: inputs,
-        texts: [inputs],
+        inputs: inputs,
+        // query: inputs,
+        // texts: [inputs],
         truncate: true,
     });
 
diff --git a/load_tests/load_grpc.js b/load_tests/load_grpc.js
index bce6834e..30d2676a 100644
--- a/load_tests/load_grpc.js
+++ b/load_tests/load_grpc.js
@@ -2,7 +2,7 @@ import {check} from 'k6';
 import grpc from 'k6/experimental/grpc';
 import {Trend} from 'k6/metrics';
 
-const host = __ENV.HOST || '127.0.0.1:8080';
+const host = __ENV.HOST || '127.0.0.1:3000';
 
 const totalTime = new Trend('total_time', true);
 const tokenizationTIme = new Trend('tokenization_time', true);
@@ -25,7 +25,7 @@ export const options = {
         // },
         load_test: {
             executor: 'constant-arrival-rate',
-            duration: '30s',
+            duration: '5m',
             preAllocatedVUs: 5000,
             rate: 1000,
             timeUnit: '1s',
diff --git a/proto/tei.proto b/proto/tei.proto
index 394c0262..aac6c2ba 100644
--- a/proto/tei.proto
+++ b/proto/tei.proto
@@ -79,6 +79,7 @@ message EmbedRequest {
     bool truncate = 2;
     bool normalize = 3;
     TruncationDirection truncation_direction = 4;
+    optional string prompt_name = 5;
 }
 
 message EmbedResponse {
@@ -90,6 +91,7 @@ message EmbedSparseRequest {
     string inputs = 1;
     bool truncate = 2;
     TruncationDirection truncation_direction = 3;
+    optional string prompt_name = 4;
 }
 
 message SparseValue {
@@ -106,6 +108,7 @@ message EmbedAllRequest {
     string inputs = 1;
     bool truncate = 2;
     TruncationDirection truncation_direction = 3;
+    optional string prompt_name = 4;
 }
 
 message TokenEmbedding {
@@ -175,6 +178,7 @@ message RerankResponse {
 message EncodeRequest {
     string inputs = 1;
     bool add_special_tokens = 2;
+    optional string prompt_name = 3;
 }
 
 message SimpleToken {
diff --git a/router/Cargo.toml b/router/Cargo.toml
index 2f2b0815..e648036b 100644
--- a/router/Cargo.toml
+++ b/router/Cargo.toml
@@ -15,38 +15,39 @@ name = "text-embeddings-router"
 path = "src/main.rs"
 
 [dependencies]
-anyhow = "1.0.71"
+anyhow = { workspace = true }
 text-embeddings-backend = { path = "../backends", features = ["clap"] }
 text-embeddings-core = { path = "../core" }
-clap = { version = "4.1.4", features = ["derive", "env"] }
+clap = { workspace = true }
 futures = "^0.3"
-init-tracing-opentelemetry = { version = "0.14.1", features = ["opentelemetry-otlp"] }
-hf-hub = { version = "0.3.0", features = ["tokio"] }
+init-tracing-opentelemetry = { version = "0.18.1", features = ["opentelemetry-otlp"] }
+hf-hub = { workspace = true }
 http = "1.0.0"
-num_cpus = "1.16.0"
-metrics = "0.21.0"
-metrics-exporter-prometheus = { version = "0.12.1", features = [] }
-opentelemetry = { version = "0.20.0", features = ["rt-tokio"] }
-opentelemetry-otlp = "0.13.0"
-reqwest = { version = "0.11.14", features = [] }
-serde = "1.0.152"
-serde_json = "1.0.93"
-thiserror = "1.0.38"
-tokenizers = { version = "0.19.1", default-features=false, features=["onig", "esaxx_fast"] }
-tokio = { version = "1.25.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
-tracing = "0.1.37"
-tracing-opentelemetry = "0.21.0"
+num_cpus = { workspace = true }
+metrics = { workspace = true }
+metrics-exporter-prometheus = { version = "0.15.1", features = [] }
+opentelemetry = "0.23.0"
+opentelemetry_sdk = { version = "0.23.0", features = ["rt-tokio"] }
+opentelemetry-otlp = "0.16.0"
+reqwest = { version = "0.12.5", features = [] }
+simsimd = "4.4.0"
+serde = { workspace = true }
+serde_json = { workspace = true }
+thiserror = { workspace = true }
+tokenizers = { workspace = true }
+tokio = { workspace = true }
+tracing = { workspace = true }
+tracing-opentelemetry = "0.24.0"
 tracing-subscriber = { version = "0.3.16", features = ["json", "env-filter"] }
 veil = "0.1.6"
-mimalloc = { version = "*", default-features = false }
 
 # HTTP dependencies
 axum = { version = "0.7.4", features = ["json"], optional = true }
-axum-tracing-opentelemetry = { version = "0.17.0", optional = true }
-base64 = { version = "0.21.4", optional = true }
+axum-tracing-opentelemetry = { version = "0.18.1", optional = true }
+base64 = { version = "0.22.1", optional = true }
 tower-http = { version = "0.5.1", features = ["cors"], optional = true }
 utoipa = { version = "4.2", features = ["axum_extras"], optional = true }
-utoipa-swagger-ui = { version = "6.0", features = ["axum"], optional = true }
+utoipa-swagger-ui = { version = "7.1", features = ["axum"], optional = true }
 
 # gRPC dependencies
 async-stream = { version = "0.3.5", optional = true }
@@ -56,14 +57,21 @@ tonic-health = { version = "0.11.0", optional = true }
 tonic-reflection = { version = "0.11.0", optional = true }
 tokio-stream = { version = "0.1.14", optional = true }
 
+# Malloc trim hack for linux
+[target.'cfg(target_os = "linux")'.dependencies]
+libc = "0.2.149"
+# else use mimalloc
+[target.'cfg(not(target_os = "linux"))'.dependencies]
+mimalloc = { version = "*", features = ["no_thp"] }
+
 [dev-dependencies]
 insta = { git = "https://github.com/OlivierDehaene/insta", rev = "f4f98c0410b91fb5a28b10df98e4422955be9c2c", features = ["yaml"] }
 is_close = "0.1.3"
-reqwest = { version = "0.11.22", features = ["json"] }
+reqwest = { version = "0.12.5", features = ["json"] }
 
 [build-dependencies]
 vergen = { version = "8.0.0", features = ["build", "git", "gitcl"] }
-tonic-build = { version = "0.10.2", optional = true }
+tonic-build = { version = "0.11.0", optional = true }
 
 [features]
 default = ["candle", "http"]
@@ -74,6 +82,7 @@ mkl = ["text-embeddings-backend/mkl"]
 mkl-dynamic = ["text-embeddings-backend/mkl-dynamic"]
 accelerate = ["text-embeddings-backend/accelerate"]
 python = ["text-embeddings-backend/python"]
+ort = ["text-embeddings-backend/ort"]
 candle = ["text-embeddings-backend/candle"]
 candle-cuda = ["candle", "text-embeddings-backend/flash-attn"]
 candle-cuda-turing = ["candle", "text-embeddings-backend/flash-attn-v1"]
diff --git a/router/src/grpc/server.rs b/router/src/grpc/server.rs
index 98ee5601..c3b34a8e 100644
--- a/router/src/grpc/server.rs
+++ b/router/src/grpc/server.rs
@@ -87,6 +87,7 @@ impl TextEmbeddingsService {
                 request.inputs,
                 request.truncate,
                 truncation_direction,
+                request.prompt_name,
                 request.normalize,
                 permit,
             )
@@ -142,6 +143,7 @@ impl TextEmbeddingsService {
                 request.inputs,
                 request.truncate,
                 truncation_direction,
+                request.prompt_name,
                 permit,
             )
             .await
@@ -207,6 +209,7 @@ impl TextEmbeddingsService {
                 request.inputs,
                 request.truncate,
                 truncation_direction,
+                request.prompt_name,
                 permit,
             )
             .await
@@ -326,11 +329,17 @@ impl TextEmbeddingsService {
     #[instrument(skip_all)]
     async fn tokenize_inner(&self, request: EncodeRequest) -> Result<EncodeResponse, Status> {
         let inputs = request.inputs;
-        let encoding = self
+        let (encoded_inputs, encoding) = self
             .infer
-            .tokenize(inputs.clone(), request.add_special_tokens)
+            .tokenize(
+                inputs.clone(),
+                request.add_special_tokens,
+                request.prompt_name,
+            )
             .await
             .map_err(ErrorResponse::from)?;
+        let inputs = encoded_inputs.unwrap_or(inputs);
+
         let tokens: Vec<SimpleToken> = encoding
             .get_ids()
             .iter()
@@ -589,7 +598,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         &self,
         request: Request<EmbedRequest>,
     ) -> Result<Response<EmbedResponse>, Status> {
-        metrics::increment_counter!("te_request_count", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         let permit = self
             .infer
@@ -600,7 +610,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         let (response, metadata) = self.embed_pooled_inner(request, permit).await?;
         let headers = HeaderMap::from(metadata);
 
-        metrics::increment_counter!("te_request_success", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         Ok(Response::from_parts(
             MetadataMap::from_headers(headers),
@@ -629,7 +640,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         &self,
         request: Request<EmbedSparseRequest>,
     ) -> Result<Response<EmbedSparseResponse>, Status> {
-        metrics::increment_counter!("te_request_count", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         let permit = self
             .infer
@@ -640,7 +652,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         let (response, metadata) = self.embed_sparse_inner(request, permit).await?;
         let headers = HeaderMap::from(metadata);
 
-        metrics::increment_counter!("te_request_success", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         Ok(Response::from_parts(
             MetadataMap::from_headers(headers),
@@ -669,7 +682,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         &self,
         request: Request<EmbedAllRequest>,
     ) -> Result<Response<EmbedAllResponse>, Status> {
-        metrics::increment_counter!("te_request_count", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         let permit = self
             .infer
@@ -680,7 +694,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         let (response, metadata) = self.embed_all_inner(request, permit).await?;
         let headers = HeaderMap::from(metadata);
 
-        metrics::increment_counter!("te_request_success", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         Ok(Response::from_parts(
             MetadataMap::from_headers(headers),
@@ -713,7 +728,8 @@ impl grpc::predict_server::Predict for TextEmbeddingsService {
         &self,
         request: Request<PredictRequest>,
     ) -> Result<Response<PredictResponse>, Status> {
-        metrics::increment_counter!("te_request_count", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         let permit = self
             .infer
@@ -733,7 +749,8 @@ impl grpc::predict_server::Predict for TextEmbeddingsService {
             .await?;
         let headers = HeaderMap::from(metadata);
 
-        metrics::increment_counter!("te_request_success", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         Ok(Response::from_parts(
             MetadataMap::from_headers(headers),
@@ -746,7 +763,8 @@ impl grpc::predict_server::Predict for TextEmbeddingsService {
         &self,
         request: Request<PredictPairRequest>,
     ) -> Result<Response<PredictResponse>, Status> {
-        metrics::increment_counter!("te_request_count", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
         let request = request.into_inner();
 
         let mut inputs = request.inputs;
@@ -782,7 +800,8 @@ impl grpc::predict_server::Predict for TextEmbeddingsService {
             .await?;
         let headers = HeaderMap::from(metadata);
 
-        metrics::increment_counter!("te_request_success", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         Ok(Response::from_parts(
             MetadataMap::from_headers(headers),
@@ -886,20 +905,23 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
                 error: message,
                 error_type: ErrorType::Validation,
             };
-            metrics::increment_counter!("te_request_failure", "err" => "validation");
+            let counter = metrics::counter!("te_request_failure", "err" => "validation");
+            counter.increment(1);
             Err(err)?;
         }
 
         match &self.info.model_type {
             ModelType::Classifier(_) => {
-                metrics::increment_counter!("te_request_failure", "err" => "model_type");
+                let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+                counter.increment(1);
                 let message = "model is not a re-ranker model".to_string();
                 tracing::error!("{message}");
                 Err(Status::new(Code::FailedPrecondition, message))
             }
             ModelType::Reranker(_) => Ok(()),
             ModelType::Embedding(_) => {
-                metrics::increment_counter!("te_request_failure", "err" => "model_type");
+                let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+                counter.increment(1);
                 let message = "model is not a classifier model".to_string();
                 tracing::error!("{message}");
                 Err(Status::new(Code::FailedPrecondition, message))
@@ -937,7 +959,8 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
             ))
         };
 
-        metrics::increment_counter!("te_request_count", "method" => "batch");
+        let counter = metrics::counter!("te_request_count", "method" => "batch");
+        counter.increment(1);
 
         let batch_size = request.texts.len();
         if batch_size > self.info.max_client_batch_size {
@@ -950,7 +973,8 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
                 error: message,
                 error_type: ErrorType::Validation,
             };
-            metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+            let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+            counter.increment(1);
             Err(err)?;
         }
 
@@ -1015,7 +1039,8 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
 
         let batch_size = batch_size as u64;
 
-        metrics::increment_counter!("te_request_success", "method" => "batch");
+        let counter = metrics::counter!("te_request_success", "method" => "batch");
+        counter.increment(1);
 
         let response_metadata = ResponseMetadata::new(
             total_compute_chars,
@@ -1065,14 +1090,16 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
         // Check model type
         match &self.info.model_type {
             ModelType::Classifier(_) => {
-                metrics::increment_counter!("te_request_failure", "err" => "model_type");
+                let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+                counter.increment(1);
                 let message = "model is not a re-ranker model".to_string();
                 tracing::error!("{message}");
                 Err(Status::new(Code::FailedPrecondition, message))
             }
             ModelType::Reranker(_) => Ok(()),
             ModelType::Embedding(_) => {
-                metrics::increment_counter!("te_request_failure", "err" => "model_type");
+                let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+                counter.increment(1);
                 let message = "model is not a classifier model".to_string();
                 tracing::error!("{message}");
                 Err(Status::new(Code::FailedPrecondition, message))
@@ -1112,7 +1139,8 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
             ))
         };
 
-        metrics::increment_counter!("te_request_count", "method" => "batch");
+        let counter = metrics::counter!("te_request_count", "method" => "batch");
+        counter.increment(1);
 
         let mut request_stream = request.into_inner();
 
@@ -1261,7 +1289,8 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
                 error: message,
                 error_type: ErrorType::Backend,
             };
-            metrics::increment_counter!("te_request_failure", "err" => "missing_values");
+            let counter = metrics::counter!("te_request_failure", "err" => "missing_values");
+            counter.increment(1);
             Err(err)?;
         }
 
@@ -1271,7 +1300,8 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
 
         let batch_size = batch_size as u64;
 
-        metrics::increment_counter!("te_request_success", "method" => "batch");
+        let counter = metrics::counter!("te_request_success", "method" => "batch");
+        counter.increment(1);
 
         let response_metadata = ResponseMetadata::new(
             total_compute_chars,
@@ -1504,6 +1534,7 @@ impl From<ErrorResponse> for Status {
             ErrorType::Overloaded => Code::ResourceExhausted,
             ErrorType::Validation => Code::InvalidArgument,
             ErrorType::Tokenizer => Code::FailedPrecondition,
+            ErrorType::Empty => Code::InvalidArgument,
         };
 
         Status::new(code, value.error)
diff --git a/router/src/http/server.rs b/router/src/http/server.rs
index 11b3a521..ae3d8a60 100644
--- a/router/src/http/server.rs
+++ b/router/src/http/server.rs
@@ -4,8 +4,10 @@ use crate::http::types::{
     EmbedSparseRequest, EmbedSparseResponse, Embedding, EncodingFormat, Input, InputIds, InputType,
     OpenAICompatEmbedding, OpenAICompatErrorResponse, OpenAICompatRequest, OpenAICompatResponse,
     OpenAICompatUsage, PredictInput, PredictRequest, PredictResponse, Prediction, Rank,
-    RerankRequest, RerankResponse, Sequence, SimpleToken, SparseValue, TokenizeInput,
-    TokenizeRequest, TokenizeResponse, VertexPrediction, VertexRequest, VertexResponse,
+    RerankRequest, RerankResponse, Sequence, SimilarityInput, SimilarityParameters,
+    SimilarityRequest, SimilarityResponse, SimpleToken, SparseValue, TokenizeInput,
+    TokenizeRequest, TokenizeResponse, TruncationDirection, VertexPrediction, VertexRequest,
+    VertexResponse,
 };
 use crate::{
     shutdown, ClassifierModel, EmbeddingModel, ErrorResponse, ErrorType, Info, ModelType,
@@ -25,6 +27,7 @@ use futures::future::join_all;
 use futures::FutureExt;
 use http::header::AUTHORIZATION;
 use metrics_exporter_prometheus::{PrometheusBuilder, PrometheusHandle};
+use simsimd::SpatialSimilarity;
 use std::net::SocketAddr;
 use std::time::{Duration, Instant};
 use text_embeddings_backend::BackendError;
@@ -32,7 +35,6 @@ use text_embeddings_core::infer::{
     AllEmbeddingsInferResponse, Infer, InferMetadata, PooledEmbeddingsInferResponse,
 };
 use text_embeddings_core::TextEmbeddingsError;
-use tokenizers::TruncationDirection;
 use tokio::sync::OwnedSemaphorePermit;
 use tower_http::cors::{AllowOrigin, CorsLayer};
 use tracing::instrument;
@@ -87,6 +89,8 @@ example = json ! ({"error": "Inference failed", "error_type": "backend"})),
 example = json ! ({"error": "Model is overloaded", "error_type": "overloaded"})),
 (status = 422, description = "Tokenization error", body = ErrorResponse,
 example = json ! ({"error": "Tokenization error", "error_type": "tokenizer"})),
+(status = 400, description = "Batch is empty", body = ErrorResponse,
+example = json ! ({"error": "Batch is empty", "error_type": "empty"})),
 (status = 413, description = "Batch size error", body = ErrorResponse,
 example = json ! ({"error": "Batch size error", "error_type": "validation"})),
 )
@@ -118,7 +122,7 @@ async fn predict(
             .predict(
                 inputs,
                 truncate,
-                req.truncation_direction,
+                req.truncation_direction.into(),
                 req.raw_scores,
                 permit,
             )
@@ -163,14 +167,16 @@ async fn predict(
 
     let (response, metadata) = match req.inputs {
         PredictInput::Single(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             let compute_chars = inputs.count_chars();
             let permit = infer.try_acquire_permit().map_err(ErrorResponse::from)?;
             let (prompt_tokens, tokenization, queue, inference, predictions) =
                 predict_inner(inputs, truncate, infer.0, info.0, Some(permit)).await?;
 
-            metrics::increment_counter!("te_request_success", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             (
                 PredictResponse::Single(predictions),
@@ -185,7 +191,8 @@ async fn predict(
             )
         }
         PredictInput::Batch(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "batch");
+            let counter = metrics::counter!("te_request_count", "method" => "batch");
+            counter.increment(1);
 
             let batch_size = inputs.len();
             if batch_size > info.max_client_batch_size {
@@ -198,7 +205,8 @@ async fn predict(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -237,7 +245,8 @@ async fn predict(
             }
             let batch_size = batch_size as u64;
 
-            metrics::increment_counter!("te_request_success", "method" => "batch");
+            let counter = metrics::counter!("te_request_success", "method" => "batch");
+            counter.increment(1);
 
             (
                 PredictResponse::Batch(predictions),
@@ -278,6 +287,8 @@ example = json ! ({"error": "Inference failed", "error_type": "backend"})),
 example = json ! ({"error": "Model is overloaded", "error_type": "overloaded"})),
 (status = 422, description = "Tokenization error", body = ErrorResponse,
 example = json ! ({"error": "Tokenization error", "error_type": "tokenizer"})),
+(status = 400, description = "Batch is empty", body = ErrorResponse,
+example = json ! ({"error": "Batch is empty", "error_type": "empty"})),
 (status = 413, description = "Batch size error", body = ErrorResponse,
 example = json ! ({"error": "Batch size error", "error_type": "validation"})),
 )
@@ -299,16 +310,18 @@ async fn rerank(
         tracing::error!("{message}");
         let err = ErrorResponse {
             error: message,
-            error_type: ErrorType::Validation,
+            error_type: ErrorType::Empty,
         };
-        metrics::increment_counter!("te_request_failure", "err" => "validation");
+        let counter = metrics::counter!("te_request_failure", "err" => "validation");
+        counter.increment(1);
         Err(err)?;
     }
 
     match &info.model_type {
         ModelType::Reranker(_) => Ok(()),
         ModelType::Classifier(_) | ModelType::Embedding(_) => {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "model is not a re-ranker model".to_string();
             Err(TextEmbeddingsError::Backend(BackendError::Inference(
                 message,
@@ -328,7 +341,7 @@ async fn rerank(
             .predict(
                 (query, text),
                 truncate,
-                req.truncation_direction,
+                req.truncation_direction.into(),
                 req.raw_scores,
                 permit,
             )
@@ -349,7 +362,8 @@ async fn rerank(
     let truncate = req.truncate.unwrap_or(info.auto_truncate);
 
     let (response, metadata) = {
-        metrics::increment_counter!("te_request_count", "method" => "batch");
+        let counter = metrics::counter!("te_request_count", "method" => "batch");
+        counter.increment(1);
 
         let batch_size = req.texts.len();
         if batch_size > info.max_client_batch_size {
@@ -362,7 +376,8 @@ async fn rerank(
                 error: message,
                 error_type: ErrorType::Validation,
             };
-            metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+            let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+            counter.increment(1);
             Err(err)?;
         }
 
@@ -420,7 +435,8 @@ async fn rerank(
 
         let batch_size = batch_size as u64;
 
-        metrics::increment_counter!("te_request_success", "method" => "batch");
+        let counter = metrics::counter!("te_request_success", "method" => "batch");
+        counter.increment(1);
 
         (
             RerankResponse(ranks),
@@ -445,6 +461,90 @@ async fn rerank(
     Ok((headers, Json(response)))
 }
 
+/// Get Sentence Similarity. Returns a 424 status code if the model is not an embedding model.
+#[utoipa::path(
+post,
+tag = "Text Embeddings Inference",
+path = "/similarity",
+request_body = SimilarityRequest,
+responses(
+(status = 200, description = "Sentence Similarity", body = SimilarityResponse),
+(status = 424, description = "Embedding Error", body = ErrorResponse,
+example = json ! ({"error": "Inference failed", "error_type": "backend"})),
+(status = 429, description = "Model is overloaded", body = ErrorResponse,
+example = json ! ({"error": "Model is overloaded", "error_type": "overloaded"})),
+(status = 422, description = "Tokenization error", body = ErrorResponse,
+example = json ! ({"error": "Tokenization error", "error_type": "tokenizer"})),
+(status = 400, description = "Batch is empty", body = ErrorResponse,
+example = json ! ({"error": "Batch is empty", "error_type": "empty"})),
+(status = 413, description = "Batch size error", body = ErrorResponse,
+example = json ! ({"error": "Batch size error", "error_type": "validation"})),
+)
+)]
+#[instrument(
+    skip_all,
+    fields(total_time, tokenization_time, queue_time, inference_time,)
+)]
+async fn similarity(
+    infer: Extension<Infer>,
+    info: Extension<Info>,
+    Json(req): Json<SimilarityRequest>,
+) -> Result<(HeaderMap, Json<SimilarityResponse>), (StatusCode, Json<ErrorResponse>)> {
+    if req.inputs.sentences.is_empty() {
+        let message = "`inputs.sentences` cannot be empty".to_string();
+        tracing::error!("{message}");
+        let err = ErrorResponse {
+            error: message,
+            error_type: ErrorType::Empty,
+        };
+        let counter = metrics::counter!("te_request_failure", "err" => "validation");
+        counter.increment(1);
+        Err(err)?;
+    }
+    // +1 because of the source sentence
+    let batch_size = req.inputs.sentences.len() + 1;
+    if batch_size > info.max_client_batch_size {
+        let message = format!(
+            "batch size {batch_size} > maximum allowed batch size {}",
+            info.max_client_batch_size
+        );
+        tracing::error!("{message}");
+        let err = ErrorResponse {
+            error: message,
+            error_type: ErrorType::Validation,
+        };
+        let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+        counter.increment(1);
+        Err(err)?;
+    }
+
+    // Convert request to embed request
+    let mut inputs = Vec::with_capacity(req.inputs.sentences.len() + 1);
+    inputs.push(InputType::String(req.inputs.source_sentence));
+    for s in req.inputs.sentences {
+        inputs.push(InputType::String(s));
+    }
+    let parameters = req.parameters.unwrap_or_default();
+    let embed_req = EmbedRequest {
+        inputs: Input::Batch(inputs),
+        truncate: parameters.truncate,
+        truncation_direction: parameters.truncation_direction,
+        prompt_name: parameters.prompt_name,
+        normalize: false,
+    };
+
+    // Get embeddings
+    let (header_map, embed_response) = embed(infer, info, Json(embed_req)).await?;
+    let embeddings = embed_response.0 .0;
+
+    // Compute cosine
+    let distances = (1..batch_size)
+        .map(|i| 1.0 - f32::cosine(&embeddings[0], &embeddings[i]).unwrap() as f32)
+        .collect();
+
+    Ok((header_map, Json(SimilarityResponse(distances))))
+}
+
 /// Get Embeddings. Returns a 424 status code if the model is not an embedding model.
 #[utoipa::path(
 post,
@@ -459,6 +559,8 @@ example = json ! ({"error": "Inference failed", "error_type": "backend"})),
 example = json ! ({"error": "Model is overloaded", "error_type": "overloaded"})),
 (status = 422, description = "Tokenization error", body = ErrorResponse,
 example = json ! ({"error": "Tokenization error", "error_type": "tokenizer"})),
+(status = 400, description = "Batch is empty", body = ErrorResponse,
+example = json ! ({"error": "Batch is empty", "error_type": "empty"})),
 (status = 413, description = "Batch size error", body = ErrorResponse,
 example = json ! ({"error": "Batch size error", "error_type": "validation"})),
 )
@@ -479,7 +581,8 @@ async fn embed(
 
     let (response, metadata) = match req.inputs {
         Input::Single(input) => {
-            metrics::increment_counter!("te_request_count", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             let compute_chars = input.count_chars();
 
@@ -488,14 +591,16 @@ async fn embed(
                 .embed_pooled(
                     input,
                     truncate,
-                    req.truncation_direction,
+                    req.truncation_direction.into(),
+                    req.prompt_name,
                     req.normalize,
                     permit,
                 )
                 .await
                 .map_err(ErrorResponse::from)?;
 
-            metrics::increment_counter!("te_request_success", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             (
                 EmbedResponse(vec![response.results]),
@@ -510,16 +615,18 @@ async fn embed(
             )
         }
         Input::Batch(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "batch");
+            let counter = metrics::counter!("te_request_count", "method" => "batch");
+            counter.increment(1);
 
             if inputs.is_empty() {
                 let message = "`inputs` cannot be empty".to_string();
                 tracing::error!("{message}");
                 let err = ErrorResponse {
                     error: message,
-                    error_type: ErrorType::Validation,
+                    error_type: ErrorType::Empty,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -534,7 +641,8 @@ async fn embed(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -545,13 +653,15 @@ async fn embed(
                 compute_chars += input.count_chars();
 
                 let local_infer = infer.clone();
+                let prompt_name = req.prompt_name.clone();
                 futures.push(async move {
                     let permit = local_infer.acquire_permit().await;
                     local_infer
                         .embed_pooled(
                             input,
                             truncate,
-                            req.truncation_direction,
+                            req.truncation_direction.into(),
+                            prompt_name,
                             req.normalize,
                             permit,
                         )
@@ -579,7 +689,8 @@ async fn embed(
             }
             let batch_size = batch_size as u64;
 
-            metrics::increment_counter!("te_request_success", "method" => "batch");
+            let counter = metrics::counter!("te_request_success", "method" => "batch");
+            counter.increment(1);
 
             (
                 EmbedResponse(embeddings),
@@ -619,6 +730,8 @@ example = json ! ({"error": "Inference failed", "error_type": "backend"})),
 example = json ! ({"error": "Model is overloaded", "error_type": "overloaded"})),
 (status = 422, description = "Tokenization error", body = ErrorResponse,
 example = json ! ({"error": "Tokenization error", "error_type": "tokenizer"})),
+(status = 400, description = "Batch is empty", body = ErrorResponse,
+example = json ! ({"error": "Batch is empty", "error_type": "empty"})),
 (status = 413, description = "Batch size error", body = ErrorResponse,
 example = json ! ({"error": "Batch size error", "error_type": "validation"})),
 )
@@ -648,17 +761,25 @@ async fn embed_sparse(
 
     let (response, metadata) = match req.inputs {
         Input::Single(input) => {
-            metrics::increment_counter!("te_request_count", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             let compute_chars = input.count_chars();
 
             let permit = infer.try_acquire_permit().map_err(ErrorResponse::from)?;
             let response = infer
-                .embed_sparse(input, truncate, req.truncation_direction, permit)
+                .embed_sparse(
+                    input,
+                    truncate,
+                    req.truncation_direction.into(),
+                    req.prompt_name,
+                    permit,
+                )
                 .await
                 .map_err(ErrorResponse::from)?;
 
-            metrics::increment_counter!("te_request_success", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             (
                 EmbedSparseResponse(vec![sparsify(response.results)]),
@@ -673,16 +794,18 @@ async fn embed_sparse(
             )
         }
         Input::Batch(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "batch");
+            let counter = metrics::counter!("te_request_count", "method" => "batch");
+            counter.increment(1);
 
             if inputs.is_empty() {
                 let message = "`inputs` cannot be empty".to_string();
                 tracing::error!("{message}");
                 let err = ErrorResponse {
                     error: message,
-                    error_type: ErrorType::Validation,
+                    error_type: ErrorType::Empty,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -697,7 +820,8 @@ async fn embed_sparse(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -708,10 +832,17 @@ async fn embed_sparse(
                 compute_chars += input.count_chars();
 
                 let local_infer = infer.clone();
+                let prompt_name = req.prompt_name.clone();
                 futures.push(async move {
                     let permit = local_infer.acquire_permit().await;
                     let response = local_infer
-                        .embed_sparse(input, truncate, req.truncation_direction, permit)
+                        .embed_sparse(
+                            input,
+                            truncate,
+                            req.truncation_direction.into(),
+                            prompt_name,
+                            permit,
+                        )
                         .await?;
                     Ok((sparsify(response.results), response.metadata))
                 })
@@ -737,7 +868,8 @@ async fn embed_sparse(
             }
             let batch_size = batch_size as u64;
 
-            metrics::increment_counter!("te_request_success", "method" => "batch");
+            let counter = metrics::counter!("te_request_success", "method" => "batch");
+            counter.increment(1);
 
             (
                 EmbedSparseResponse(embeddings),
@@ -778,6 +910,8 @@ example = json ! ({"error": "Inference failed", "error_type": "backend"})),
 example = json ! ({"error": "Model is overloaded", "error_type": "overloaded"})),
 (status = 422, description = "Tokenization error", body = ErrorResponse,
 example = json ! ({"error": "Tokenization error", "error_type": "tokenizer"})),
+(status = 400, description = "Batch is empty", body = ErrorResponse,
+example = json ! ({"error": "Batch is empty", "error_type": "empty"})),
 (status = 413, description = "Batch size error", body = ErrorResponse,
 example = json ! ({"error": "Batch size error", "error_type": "validation"})),
 )
@@ -798,17 +932,25 @@ async fn embed_all(
 
     let (response, metadata) = match req.inputs {
         Input::Single(input) => {
-            metrics::increment_counter!("te_request_count", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             let compute_chars = input.count_chars();
 
             let permit = infer.try_acquire_permit().map_err(ErrorResponse::from)?;
             let response = infer
-                .embed_all(input, truncate, req.truncation_direction, permit)
+                .embed_all(
+                    input,
+                    truncate,
+                    req.truncation_direction.into(),
+                    req.prompt_name,
+                    permit,
+                )
                 .await
                 .map_err(ErrorResponse::from)?;
 
-            metrics::increment_counter!("te_request_success", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             (
                 EmbedAllResponse(vec![response.results]),
@@ -823,16 +965,18 @@ async fn embed_all(
             )
         }
         Input::Batch(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "batch");
+            let counter = metrics::counter!("te_request_count", "method" => "batch");
+            counter.increment(1);
 
             if inputs.is_empty() {
                 let message = "`inputs` cannot be empty".to_string();
                 tracing::error!("{message}");
                 let err = ErrorResponse {
                     error: message,
-                    error_type: ErrorType::Validation,
+                    error_type: ErrorType::Empty,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -847,7 +991,8 @@ async fn embed_all(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -858,10 +1003,17 @@ async fn embed_all(
                 compute_chars += input.count_chars();
 
                 let local_infer = infer.clone();
+                let prompt_name = req.prompt_name.clone();
                 futures.push(async move {
                     let permit = local_infer.acquire_permit().await;
                     local_infer
-                        .embed_all(input, truncate, req.truncation_direction, permit)
+                        .embed_all(
+                            input,
+                            truncate,
+                            req.truncation_direction.into(),
+                            prompt_name,
+                            permit,
+                        )
                         .await
                 })
             }
@@ -886,7 +1038,8 @@ async fn embed_all(
             }
             let batch_size = batch_size as u64;
 
-            metrics::increment_counter!("te_request_success", "method" => "batch");
+            let counter = metrics::counter!("te_request_success", "method" => "batch");
+            counter.increment(1);
 
             (
                 EmbedAllResponse(embeddings),
@@ -926,6 +1079,8 @@ example = json ! ({"message": "Inference failed", "type": "backend"})),
 example = json ! ({"message": "Model is overloaded", "type": "overloaded"})),
 (status = 422, description = "Tokenization error", body = OpenAICompatErrorResponse,
 example = json ! ({"message": "Tokenization error", "type": "tokenizer"})),
+(status = 400, description = "Batch is empty", body = OpenAICompatErrorResponse,
+example = json ! ({"message": "Batch is empty", "type": "empty"})),
 (status = 413, description = "Batch size error", body = OpenAICompatErrorResponse,
 example = json ! ({"message": "Batch size error", "type": "validation"})),
 )
@@ -962,17 +1117,26 @@ async fn openai_embed(
 
     let (embeddings, metadata) = match req.input {
         Input::Single(input) => {
-            metrics::increment_counter!("te_request_count", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             let compute_chars = input.count_chars();
 
             let permit = infer.try_acquire_permit().map_err(ErrorResponse::from)?;
             let response = infer
-                .embed_pooled(input, truncate, TruncationDirection::Right, true, permit)
+                .embed_pooled(
+                    input,
+                    truncate,
+                    tokenizers::TruncationDirection::Right,
+                    None,
+                    true,
+                    permit,
+                )
                 .await
                 .map_err(ErrorResponse::from)?;
 
-            metrics::increment_counter!("te_request_success", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             let embedding = encode_embedding(response.results);
             (
@@ -992,16 +1156,18 @@ async fn openai_embed(
             )
         }
         Input::Batch(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "batch");
+            let counter = metrics::counter!("te_request_count", "method" => "batch");
+            counter.increment(1);
 
             if inputs.is_empty() {
                 let message = "`inputs` cannot be empty".to_string();
                 tracing::error!("{message}");
                 let err = ErrorResponse {
                     error: message,
-                    error_type: ErrorType::Validation,
+                    error_type: ErrorType::Empty,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -1016,7 +1182,8 @@ async fn openai_embed(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -1030,7 +1197,14 @@ async fn openai_embed(
                 futures.push(async move {
                     let permit = local_infer.acquire_permit().await;
                     local_infer
-                        .embed_pooled(input, truncate, TruncationDirection::Right, true, permit)
+                        .embed_pooled(
+                            input,
+                            truncate,
+                            tokenizers::TruncationDirection::Right,
+                            None,
+                            true,
+                            permit,
+                        )
                         .await
                 })
             }
@@ -1060,7 +1234,8 @@ async fn openai_embed(
             }
             let batch_size = batch_size as u64;
 
-            metrics::increment_counter!("te_request_success", "method" => "batch");
+            let counter = metrics::counter!("te_request_success", "method" => "batch");
+            counter.increment(1);
 
             (
                 embeddings,
@@ -1104,8 +1279,12 @@ path = "/tokenize",
 request_body = TokenizeRequest,
 responses(
 (status = 200, description = "Tokenized ids", body = TokenizeResponse),
+(status = 400, description = "Batch is empty", body = ErrorResponse,
+example = json ! ({"error": "Batch is empty", "error_type": "empty"})),
+(status = 413, description = "Batch size error", body = ErrorResponse,
+example = json ! ({"error": "Batch size error", "error_type": "validation"})),
 (status = 422, description = "Tokenization error", body = ErrorResponse,
-example = json ! ({"message": "Tokenization error", "type": "tokenizer"})),
+example = json ! ({"error": "Tokenization error", "error_type": "tokenizer"})),
 )
 )]
 #[instrument(skip_all)]
@@ -1114,11 +1293,16 @@ async fn tokenize(
     info: Extension<Info>,
     Json(req): Json<TokenizeRequest>,
 ) -> Result<Json<TokenizeResponse>, (StatusCode, Json<ErrorResponse>)> {
-    let tokenize_inner = move |input: String, add_special_tokens: bool, infer: Infer| async move {
-        let encoding = infer
-            .tokenize(input.clone(), add_special_tokens)
+    let tokenize_inner = move |input: String,
+                               add_special_tokens: bool,
+                               prompt_name: Option<String>,
+                               infer: Infer| async move {
+        let (encoded_input, encoding) = infer
+            .tokenize(input.clone(), add_special_tokens, prompt_name)
             .await
             .map_err(ErrorResponse::from)?;
+        let input = encoded_input.unwrap_or(input);
+
         let tokens: Vec<SimpleToken> = encoding
             .get_ids()
             .iter()
@@ -1153,7 +1337,7 @@ async fn tokenize(
 
     let tokens = match req.inputs {
         TokenizeInput::Single(input) => {
-            vec![tokenize_inner(input, req.add_special_tokens, infer.0).await?]
+            vec![tokenize_inner(input, req.add_special_tokens, req.prompt_name, infer.0).await?]
         }
         TokenizeInput::Batch(inputs) => {
             if inputs.is_empty() {
@@ -1161,9 +1345,10 @@ async fn tokenize(
                 tracing::error!("{message}");
                 let err = ErrorResponse {
                     error: message,
-                    error_type: ErrorType::Validation,
+                    error_type: ErrorType::Empty,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -1178,7 +1363,8 @@ async fn tokenize(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -1187,6 +1373,7 @@ async fn tokenize(
                 futures.push(tokenize_inner(
                     input,
                     req.add_special_tokens,
+                    req.prompt_name.clone(),
                     infer.0.clone(),
                 ));
             }
@@ -1208,8 +1395,12 @@ path = "/decode",
 request_body = DecodeRequest,
 responses(
 (status = 200, description = "Decoded ids", body = DecodeResponse),
+(status = 400, description = "Batch is empty", body = ErrorResponse,
+example = json ! ({"error": "Batch is empty", "error_type": "empty"})),
+(status = 413, description = "Batch size error", body = ErrorResponse,
+example = json ! ({"error": "Batch size error", "error_type": "validation"})),
 (status = 422, description = "Tokenization error", body = ErrorResponse,
-example = json ! ({"message": "Tokenization error", "type": "tokenizer"})),
+example = json ! ({"error": "Tokenization error", "error_type": "tokenizer"})),
 )
 )]
 #[instrument(skip_all)]
@@ -1234,9 +1425,10 @@ async fn decode(
                 tracing::error!("{message}");
                 let err = ErrorResponse {
                     error: message,
-                    error_type: ErrorType::Validation,
+                    error_type: ErrorType::Empty,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -1251,7 +1443,8 @@ async fn decode(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -1283,6 +1476,8 @@ example = json ! ({"error": "Inference failed", "error_type": "backend"})),
 example = json ! ({"error": "Model is overloaded", "error_type": "overloaded"})),
 (status = 422, description = "Tokenization error", body = ErrorResponse,
 example = json ! ({"error": "Tokenization error", "error_type": "tokenizer"})),
+(status = 400, description = "Batch is empty", body = ErrorResponse,
+example = json ! ({"error": "Batch is empty", "error_type": "empty"})),
 (status = 413, description = "Batch size error", body = ErrorResponse,
 example = json ! ({"error": "Batch size error", "error_type": "validation"})),
 )
@@ -1385,6 +1580,7 @@ pub async fn run(
     embed_all,
     embed_sparse,
     openai_embed,
+    similarity,
     tokenize,
     decode,
     metrics,
@@ -1396,6 +1592,8 @@ pub async fn run(
     Info,
     ModelType,
     ClassifierModel,
+    Embedding,
+    EncodingFormat,
     EmbeddingModel,
     PredictRequest,
     Prediction,
@@ -1419,6 +1617,11 @@ pub async fn run(
     TokenizeInput,
     TokenizeRequest,
     TokenizeResponse,
+    TruncationDirection,
+    SimilarityInput,
+    SimilarityParameters,
+    SimilarityRequest,
+    SimilarityResponse,
     SimpleToken,
     InputType,
     InputIds,
@@ -1444,16 +1647,23 @@ pub async fn run(
     // map to go inside the option and then map to parse from String to HeaderValue
     // Finally, convert to AllowOrigin
     let allow_origin: Option<AllowOrigin> = cors_allow_origin.map(|cors_allow_origin| {
-        AllowOrigin::list(
-            cors_allow_origin
-                .into_iter()
-                .map(|origin| origin.parse::<HeaderValue>().unwrap()),
-        )
+        if cors_allow_origin.iter().any(|origin| origin == "*") {
+            AllowOrigin::any()
+        } else {
+            AllowOrigin::list(
+                cors_allow_origin
+                    .into_iter()
+                    .map(|origin| origin.parse::<HeaderValue>().unwrap()),
+            )
+        }
     });
 
-    let prom_handle = prom_builder
-        .install_recorder()
-        .context("failed to install metrics recorder")?;
+    // See: https://github.com/metrics-rs/metrics/issues/467#issuecomment-2022755151
+    let (recorder, _) = prom_builder
+        .build()
+        .context("failed to build prometheus recorder")?;
+    let prom_handle = recorder.handle();
+    metrics::set_global_recorder(recorder).context("Failed to set global recorder")?;
 
     // CORS layer
     let allow_origin = allow_origin.unwrap_or(AllowOrigin::any());
@@ -1483,9 +1693,7 @@ pub async fn run(
         ApiDoc::openapi()
     };
 
-    // Create router
-    let mut app = Router::new()
-        .merge(SwaggerUi::new("/docs").url("/api-doc/openapi.json", doc))
+    let mut routes = Router::new()
         // Base routes
         .route("/info", get(get_model_info))
         .route("/embed", post(embed))
@@ -1493,13 +1701,17 @@ pub async fn run(
         .route("/embed_sparse", post(embed_sparse))
         .route("/predict", post(predict))
         .route("/rerank", post(rerank))
+        .route("/similarity", post(similarity))
         .route("/tokenize", post(tokenize))
         .route("/decode", post(decode))
         // OpenAI compat route
         .route("/embeddings", post(openai_embed))
         .route("/v1/embeddings", post(openai_embed))
         // Vertex compat route
-        .route("/vertex", post(vertex_compatibility))
+        .route("/vertex", post(vertex_compatibility));
+
+    #[allow(unused_mut)]
+    let mut public_routes = Router::new()
         // Base Health route
         .route("/health", get(health))
         // Inference API health route
@@ -1507,9 +1719,7 @@ pub async fn run(
         // AWS Sagemaker health route
         .route("/ping", get(health))
         // Prometheus metrics route
-        .route("/metrics", get(metrics))
-        // Update payload limit
-        .layer(DefaultBodyLimit::max(payload_limit));
+        .route("/metrics", get(metrics));
 
     #[cfg(feature = "google")]
     {
@@ -1517,35 +1727,44 @@ pub async fn run(
 
         if let Ok(env_predict_route) = std::env::var("AIP_PREDICT_ROUTE") {
             tracing::info!("Serving Vertex compatible route on {env_predict_route}");
-            app = app.route(&env_predict_route, post(vertex_compatibility));
+            routes = routes.route(&env_predict_route, post(vertex_compatibility));
         }
 
         if let Ok(env_health_route) = std::env::var("AIP_HEALTH_ROUTE") {
             tracing::info!("Serving Vertex compatible health route on {env_health_route}");
-            app = app.route(&env_health_route, get(health));
+            public_routes = public_routes.route(&env_health_route, get(health));
         }
     }
     #[cfg(not(feature = "google"))]
     {
         // Set default routes
-        app = match &info.model_type {
+        routes = match &info.model_type {
             ModelType::Classifier(_) => {
-                app.route("/", post(predict))
+                routes
+                    .route("/", post(predict))
                     // AWS Sagemaker route
                     .route("/invocations", post(predict))
             }
             ModelType::Reranker(_) => {
-                app.route("/", post(rerank))
+                routes
+                    .route("/", post(rerank))
                     // AWS Sagemaker route
                     .route("/invocations", post(rerank))
             }
             ModelType::Embedding(model) => {
-                if model.pooling == "splade" {
-                    app.route("/", post(embed_sparse))
+                if std::env::var("TASK").ok() == Some("sentence-similarity".to_string()) {
+                    routes
+                        .route("/", post(similarity))
+                        // AWS Sagemaker route
+                        .route("/invocations", post(similarity))
+                } else if model.pooling == "splade" {
+                    routes
+                        .route("/", post(embed_sparse))
                         // AWS Sagemaker route
                         .route("/invocations", post(embed_sparse))
                 } else {
-                    app.route("/", post(embed))
+                    routes
+                        .route("/", post(embed))
                         // AWS Sagemaker route
                         .route("/invocations", post(embed))
                 }
@@ -1553,16 +1772,8 @@ pub async fn run(
         };
     }
 
-    app = app
-        .layer(Extension(infer))
-        .layer(Extension(info))
-        .layer(Extension(prom_handle.clone()))
-        .layer(OtelAxumLayer::default())
-        .layer(cors_layer);
-
     if let Some(api_key) = api_key {
-        let mut prefix = "Bearer ".to_string();
-        prefix.push_str(&api_key);
+        let prefix = format!("Bearer {}", api_key);
 
         // Leak to allow FnMut
         let api_key: &'static str = prefix.leak();
@@ -1579,9 +1790,20 @@ pub async fn run(
             }
         };
 
-        app = app.layer(axum::middleware::from_fn(auth));
+        routes = routes.layer(axum::middleware::from_fn(auth));
     }
 
+    let app = Router::new()
+        .merge(SwaggerUi::new("/docs").url("/api-doc/openapi.json", doc))
+        .merge(routes)
+        .merge(public_routes)
+        .layer(Extension(infer))
+        .layer(Extension(info))
+        .layer(Extension(prom_handle.clone()))
+        .layer(OtelAxumLayer::default())
+        .layer(DefaultBodyLimit::max(payload_limit))
+        .layer(cors_layer);
+
     // Run server
     let listener = tokio::net::TcpListener::bind(&addr)
         .await
@@ -1606,6 +1828,7 @@ impl From<&ErrorType> for StatusCode {
             ErrorType::Overloaded => StatusCode::TOO_MANY_REQUESTS,
             ErrorType::Tokenizer => StatusCode::UNPROCESSABLE_ENTITY,
             ErrorType::Validation => StatusCode::PAYLOAD_TOO_LARGE,
+            ErrorType::Empty => StatusCode::BAD_REQUEST,
         }
     }
 }
diff --git a/router/src/http/types.rs b/router/src/http/types.rs
index a2a773e8..60122888 100644
--- a/router/src/http/types.rs
+++ b/router/src/http/types.rs
@@ -4,7 +4,6 @@ use serde::{de, Deserialize, Deserializer, Serialize};
 use serde_json::json;
 use std::fmt::Formatter;
 use text_embeddings_core::tokenization::EncodingInput;
-use tokenizers::TruncationDirection;
 use utoipa::openapi::{RefOr, Schema};
 use utoipa::ToSchema;
 
@@ -194,6 +193,22 @@ impl<'__s> ToSchema<'__s> for PredictInput {
     }
 }
 
+#[derive(Debug, Clone, Copy, PartialEq, Deserialize, ToSchema, Eq, Default)]
+pub(crate) enum TruncationDirection {
+    Left,
+    #[default]
+    Right,
+}
+
+impl From<TruncationDirection> for tokenizers::TruncationDirection {
+    fn from(value: TruncationDirection) -> Self {
+        match value {
+            TruncationDirection::Left => Self::Left,
+            TruncationDirection::Right => Self::Right,
+        }
+    }
+}
+
 #[derive(Deserialize, ToSchema)]
 pub(crate) struct PredictRequest {
     pub inputs: PredictInput,
@@ -262,6 +277,7 @@ pub(crate) enum InputType {
     String(String),
     Ids(Vec<u32>),
 }
+
 impl InputType {
     pub(crate) fn count_chars(&self) -> usize {
         match self {
@@ -270,6 +286,7 @@ impl InputType {
         }
     }
 }
+
 impl From<InputType> for EncodingInput {
     fn from(value: InputType) -> Self {
         match value {
@@ -278,6 +295,7 @@ impl From<InputType> for EncodingInput {
         }
     }
 }
+
 #[derive(Deserialize, ToSchema)]
 #[serde(untagged)]
 pub(crate) enum Input {
@@ -342,6 +360,49 @@ pub(crate) struct OpenAICompatResponse {
     pub usage: OpenAICompatUsage,
 }
 
+#[derive(Deserialize, ToSchema)]
+pub(crate) struct SimilarityInput {
+    /// The string that you wish to compare the other strings with. This can be a phrase, sentence,
+    /// or longer passage, depending on the model being used.
+    #[schema(example = "What is Deep Learning?")]
+    pub source_sentence: String,
+    /// A list of strings which will be compared against the source_sentence.
+    #[schema(example = json!(["What is Machine Learning?"]))]
+    pub sentences: Vec<String>,
+}
+
+#[derive(Deserialize, ToSchema, Default)]
+pub(crate) struct SimilarityParameters {
+    #[schema(default = "false", example = "false", nullable = true)]
+    pub truncate: Option<bool>,
+    #[serde(default)]
+    #[schema(default = "right", example = "right")]
+    pub truncation_direction: TruncationDirection,
+    /// The name of the prompt that should be used by for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// Must be a key in the `sentence-transformers` configuration `prompts` dictionary.
+    ///
+    /// For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...},
+    /// then the sentence "What is the capital of France?" will be encoded as
+    /// "query: What is the capital of France?" because the prompt text will be prepended before
+    /// any text to encode.
+    #[schema(default = "null", example = "null", nullable = true)]
+    pub prompt_name: Option<String>,
+}
+
+#[derive(Deserialize, ToSchema)]
+pub(crate) struct SimilarityRequest {
+    pub inputs: SimilarityInput,
+    /// Additional inference parameters for Sentence Similarity
+    #[schema(default = "null", example = "null", nullable = true)]
+    pub parameters: Option<SimilarityParameters>,
+}
+
+#[derive(Serialize, ToSchema)]
+#[schema(example = json!([0.0, 1.0, 0.5]))]
+pub(crate) struct SimilarityResponse(pub Vec<f32>);
+
 #[derive(Deserialize, ToSchema)]
 pub(crate) struct EmbedRequest {
     pub inputs: Input,
@@ -351,6 +412,17 @@ pub(crate) struct EmbedRequest {
     #[serde(default)]
     #[schema(default = "right", example = "right")]
     pub truncation_direction: TruncationDirection,
+    /// The name of the prompt that should be used by for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// Must be a key in the `sentence-transformers` configuration `prompts` dictionary.
+    ///
+    /// For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...},
+    /// then the sentence "What is the capital of France?" will be encoded as
+    /// "query: What is the capital of France?" because the prompt text will be prepended before
+    /// any text to encode.
+    #[schema(default = "null", example = "null", nullable = true)]
+    pub prompt_name: Option<String>,
     #[serde(default = "default_normalize")]
     #[schema(default = "true", example = "true")]
     pub normalize: bool,
@@ -373,6 +445,17 @@ pub(crate) struct EmbedSparseRequest {
     #[serde(default)]
     #[schema(default = "right", example = "right")]
     pub truncation_direction: TruncationDirection,
+    /// The name of the prompt that should be used by for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// Must be a key in the `sentence-transformers` configuration `prompts` dictionary.
+    ///
+    /// For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...},
+    /// then the sentence "What is the capital of France?" will be encoded as
+    /// "query: What is the capital of France?" because the prompt text will be prepended before
+    /// any text to encode.
+    #[schema(default = "null", example = "null", nullable = true)]
+    pub prompt_name: Option<String>,
 }
 
 #[derive(Serialize, ToSchema)]
@@ -393,6 +476,17 @@ pub(crate) struct EmbedAllRequest {
     #[serde(default)]
     #[schema(default = "right", example = "right")]
     pub truncation_direction: TruncationDirection,
+    /// The name of the prompt that should be used by for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// Must be a key in the `sentence-transformers` configuration `prompts` dictionary.
+    ///
+    /// For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...},
+    /// then the sentence "What is the capital of France?" will be encoded as
+    /// "query: What is the capital of France?" because the prompt text will be prepended before
+    /// any text to encode.
+    #[schema(default = "null", example = "null", nullable = true)]
+    pub prompt_name: Option<String>,
 }
 
 #[derive(Serialize, ToSchema)]
@@ -420,6 +514,17 @@ pub(crate) struct TokenizeRequest {
     #[serde(default = "default_add_special_tokens")]
     #[schema(default = "true", example = "true")]
     pub add_special_tokens: bool,
+    /// The name of the prompt that should be used by for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// Must be a key in the `sentence-transformers` configuration `prompts` dictionary.
+    ///
+    /// For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...},
+    /// then the sentence "What is the capital of France?" will be encoded as
+    /// "query: What is the capital of France?" because the prompt text will be prepended before
+    /// any text to encode.
+    #[schema(default = "null", example = "null", nullable = true)]
+    pub prompt_name: Option<String>,
 }
 
 fn default_add_special_tokens() -> bool {
diff --git a/router/src/lib.rs b/router/src/lib.rs
index d2023515..b7b214e6 100644
--- a/router/src/lib.rs
+++ b/router/src/lib.rs
@@ -26,15 +26,18 @@ use std::fs;
 use std::net::{IpAddr, Ipv4Addr, SocketAddr};
 use std::path::Path;
 use std::time::{Duration, Instant};
-use text_embeddings_backend::DType;
+use text_embeddings_backend::{DType, Pool};
 use text_embeddings_core::download::{
-    download_artifacts, download_pool_config, download_st_config, ST_CONFIG_NAMES,
+    download_artifacts, download_new_st_config, download_pool_config, download_st_config,
+    ST_CONFIG_NAMES,
 };
 use text_embeddings_core::infer::Infer;
 use text_embeddings_core::queue::Queue;
 use text_embeddings_core::tokenization::Tokenization;
 use text_embeddings_core::TextEmbeddingsError;
-use tokenizers::Tokenizer;
+use tokenizers::processors::sequence::Sequence;
+use tokenizers::processors::template::TemplateProcessing;
+use tokenizers::{PostProcessorWrapper, Tokenizer};
 use tracing::Span;
 
 pub use logging::init_logging;
@@ -52,6 +55,8 @@ pub async fn run(
     max_batch_requests: Option<usize>,
     max_client_batch_size: usize,
     auto_truncate: bool,
+    default_prompt: Option<String>,
+    default_prompt_name: Option<String>,
     hf_api_token: Option<String>,
     hostname: Option<String>,
     port: u16,
@@ -86,11 +91,20 @@ pub async fn run(
         // Optionally download the pooling config.
         if pooling.is_none() {
             // If a pooling config exist, download it
-            let _ = download_pool_config(&api_repo).await;
+            let _ = download_pool_config(&api_repo).await.map_err(|err| {
+                tracing::warn!("Download failed: {err}");
+                err
+            });
         }
 
-        // Download sentence transformers config
+        // Download legacy sentence transformers config
+        // We don't warn on failure as it is a legacy file
         let _ = download_st_config(&api_repo).await;
+        // Download new sentence transformers config
+        let _ = download_new_st_config(&api_repo).await.map_err(|err| {
+            tracing::warn!("Download failed: {err}");
+            err
+        });
 
         // Download model from the Hub
         download_artifacts(&api_repo)
@@ -139,6 +153,28 @@ pub async fn run(
         "tokenizer.json not found. text-embeddings-inference only supports fast tokenizers",
     );
     tokenizer.with_padding(None);
+    // Qwen2 updates the post processor manually instead of into the tokenizer.json...
+    // https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct/blob/main/tokenization_qwen.py#L246
+    if config.model_type == "qwen2" {
+        let template = TemplateProcessing::builder()
+            .try_single("$A:0 <|endoftext|>:0")
+            .unwrap()
+            .try_pair("$A:0 <|endoftext|>:0 $B:1 <|endoftext|>:1")
+            .unwrap()
+            .special_tokens(vec![("<|endoftext|>", 151643)])
+            .build()
+            .unwrap();
+        match tokenizer.get_post_processor() {
+            None => tokenizer.with_post_processor(template),
+            Some(post_processor) => {
+                let post_processor = Sequence::new(vec![
+                    post_processor.clone(),
+                    PostProcessorWrapper::Template(template),
+                ]);
+                tokenizer.with_post_processor(post_processor)
+            }
+        };
+    }
 
     // Position IDs offset. Used for Roberta and camembert.
     let position_offset = if &config.model_type == "xlm-roberta"
@@ -169,7 +205,31 @@ pub async fn run(
     };
     tracing::info!("Maximum number of tokens per request: {max_input_length}");
 
-    let tokenization_workers = tokenization_workers.unwrap_or_else(num_cpus::get_physical);
+    let tokenization_workers = tokenization_workers.unwrap_or_else(num_cpus::get);
+
+    // Try to load new ST Config
+    let mut new_st_config: Option<NewSTConfig> = None;
+    let config_path = model_root.join("config_sentence_transformers.json");
+    if let Ok(config) = fs::read_to_string(config_path) {
+        new_st_config = Some(
+            serde_json::from_str(&config)
+                .context("Failed to parse `config_sentence_transformers.json`")?,
+        );
+    }
+    let prompts = new_st_config.and_then(|c| c.prompts);
+    let default_prompt = if let Some(default_prompt_name) = default_prompt_name.as_ref() {
+        match &prompts {
+            None => {
+                anyhow::bail!(format!("`default-prompt-name` is set to `{default_prompt_name}` but no prompts were found in the Sentence Transformers configuration"));
+            }
+            Some(prompts) if !prompts.contains_key(default_prompt_name) => {
+                anyhow::bail!(format!("`default-prompt-name` is set to `{default_prompt_name}` but it was not found in the Sentence Transformers prompts. Available prompts: {:?}", prompts.keys()));
+            }
+            Some(prompts) => prompts.get(default_prompt_name).cloned(),
+        }
+    } else {
+        default_prompt
+    };
 
     // Tokenization logic
     let tokenization = Tokenization::new(
@@ -177,19 +237,12 @@ pub async fn run(
         tokenizer,
         max_input_length,
         position_offset,
+        default_prompt,
+        prompts,
     );
 
     // Get dtype
-    let dtype = dtype.unwrap_or({
-        #[cfg(any(feature = "accelerate", feature = "mkl", feature = "mkl-dynamic"))]
-        {
-            DType::Float32
-        }
-        #[cfg(not(any(feature = "accelerate", feature = "mkl", feature = "mkl-dynamic")))]
-        {
-            DType::Float16
-        }
-    });
+    let dtype = dtype.unwrap_or_default();
 
     // Create backend
     tracing::info!("Starting model backend");
@@ -207,6 +260,14 @@ pub async fn run(
         .await
         .context("Model backend is not healthy")?;
 
+    if !backend.padded_model {
+        tracing::info!("Warming up model");
+        backend
+            .warmup(max_input_length, max_batch_tokens, max_batch_requests)
+            .await
+            .context("Model backend is not healthy")?;
+    }
+
     let max_batch_requests = backend
         .max_batch_size
         .map(|s| {
@@ -289,7 +350,7 @@ pub async fn run(
             api_key,
             cors_allow_origin,
         )
-        .await?;
+        .await
     }
 
     #[cfg(feature = "grpc")]
@@ -297,10 +358,8 @@ pub async fn run(
         // cors_allow_origin and payload_limit are not used for gRPC servers
         let _ = cors_allow_origin;
         let _ = payload_limit;
-        grpc::server::run(infer, info, addr, prom_builder, api_key).await?;
+        grpc::server::run(infer, info, addr, prom_builder, api_key).await
     }
-
-    Ok(())
 }
 
 fn get_backend_model_type(
@@ -335,15 +394,20 @@ fn get_backend_model_type(
         None => {
             // Load pooling config
             let config_path = model_root.join("1_Pooling/config.json");
-            let config = fs::read_to_string(config_path).context("The `--pooling` arg is not set and we could not find a pooling configuration (`1_Pooling/config.json`) for this model.")?;
-            let config: PoolConfig =
-                serde_json::from_str(&config).context("Failed to parse `1_Pooling/config.json`")?;
-            if config.pooling_mode_cls_token {
-                text_embeddings_backend::Pool::Cls
-            } else if config.pooling_mode_mean_tokens {
-                text_embeddings_backend::Pool::Mean
-            } else {
-                return Err(anyhow!("Pooling config {config:?} is not supported"));
+
+            match fs::read_to_string(config_path) {
+                Ok(config) => {
+                    let config: PoolConfig = serde_json::from_str(&config)
+                        .context("Failed to parse `1_Pooling/config.json`")?;
+                    Pool::try_from(config)?
+                }
+                Err(err) => {
+                    if !config.model_type.to_lowercase().contains("bert") {
+                        return Err(err).context("The `--pooling` arg is not set and we could not find a pooling configuration (`1_Pooling/config.json`) for this model.");
+                    }
+                    tracing::warn!("The `--pooling` arg is not set and we could not find a pooling configuration (`1_Pooling/config.json`) for this model but the model is a BERT variant. Defaulting to `CLS` pooling.");
+                    text_embeddings_backend::Pool::Cls
+                }
             }
         }
     };
@@ -366,8 +430,25 @@ pub struct ModelConfig {
 pub struct PoolConfig {
     pooling_mode_cls_token: bool,
     pooling_mode_mean_tokens: bool,
-    pooling_mode_max_tokens: bool,
-    pooling_mode_mean_sqrt_len_tokens: bool,
+    #[serde(default)]
+    pooling_mode_lasttoken: bool,
+}
+
+impl TryFrom<PoolConfig> for Pool {
+    type Error = anyhow::Error;
+
+    fn try_from(config: PoolConfig) -> std::result::Result<Self, Self::Error> {
+        if config.pooling_mode_cls_token {
+            return Ok(Pool::Cls);
+        }
+        if config.pooling_mode_mean_tokens {
+            return Ok(Pool::Mean);
+        }
+        if config.pooling_mode_lasttoken {
+            return Ok(Pool::LastToken);
+        }
+        Err(anyhow!("Pooling config {config:?} is not supported"))
+    }
 }
 
 #[derive(Debug, Deserialize)]
@@ -375,6 +456,11 @@ pub struct STConfig {
     pub max_seq_length: usize,
 }
 
+#[derive(Debug, Deserialize)]
+pub struct NewSTConfig {
+    pub prompts: Option<HashMap<String, String>>,
+}
+
 #[derive(Clone, Debug, Serialize)]
 #[cfg_attr(feature = "http", derive(utoipa::ToSchema))]
 pub struct EmbeddingModel {
@@ -448,6 +534,7 @@ pub enum ErrorType {
     Overloaded,
     Validation,
     Tokenizer,
+    Empty,
 }
 
 #[derive(Serialize)]
@@ -512,19 +599,14 @@ impl ResponseMetadata {
 
     fn record_metrics(&self) {
         // Metrics
-        metrics::histogram!(
-            "te_request_duration",
-            self.start_time.elapsed().as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_request_tokenization_duration",
-            self.tokenization_time.as_secs_f64()
-        );
-        metrics::histogram!("te_request_queue_duration", self.queue_time.as_secs_f64());
-        metrics::histogram!(
-            "te_request_inference_duration",
-            self.inference_time.as_secs_f64()
-        );
+        let histogram = metrics::histogram!("te_request_duration");
+        histogram.record(self.start_time.elapsed().as_secs_f64());
+        let histogram = metrics::histogram!("te_request_tokenization_duration");
+        histogram.record(self.tokenization_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_request_queue_duration");
+        histogram.record(self.queue_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_request_inference_duration");
+        histogram.record(self.inference_time.as_secs_f64());
     }
 }
 
diff --git a/router/src/logging.rs b/router/src/logging.rs
index 7d5eb11e..7a8fe810 100644
--- a/router/src/logging.rs
+++ b/router/src/logging.rs
@@ -1,8 +1,8 @@
-use opentelemetry::sdk::propagation::TraceContextPropagator;
-use opentelemetry::sdk::trace::Sampler;
-use opentelemetry::sdk::{trace, Resource};
 use opentelemetry::{global, KeyValue};
 use opentelemetry_otlp::WithExportConfig;
+use opentelemetry_sdk::propagation::TraceContextPropagator;
+use opentelemetry_sdk::trace::Sampler;
+use opentelemetry_sdk::{trace, Resource};
 use tracing_subscriber::layer::SubscriberExt;
 use tracing_subscriber::util::SubscriberInitExt;
 use tracing_subscriber::{EnvFilter, Layer};
@@ -48,7 +48,7 @@ pub fn init_logging(
                     )]))
                     .with_sampler(Sampler::AlwaysOn),
             )
-            .install_batch(opentelemetry::runtime::Tokio);
+            .install_batch(opentelemetry_sdk::runtime::Tokio);
 
         if let Ok(tracer) = tracer {
             layers.push(tracing_opentelemetry::layer().with_tracer(tracer).boxed());
diff --git a/router/src/main.rs b/router/src/main.rs
index 2cdc7095..67fb76c7 100644
--- a/router/src/main.rs
+++ b/router/src/main.rs
@@ -1,12 +1,12 @@
 use anyhow::Result;
 use clap::Parser;
-use mimalloc::MiMalloc;
 use opentelemetry::global;
 use text_embeddings_backend::DType;
 use veil::Redact;
 
+#[cfg(not(target_os = "linux"))]
 #[global_allocator]
-static GLOBAL: MiMalloc = MiMalloc;
+static GLOBAL: mimalloc::MiMalloc = mimalloc::MiMalloc;
 
 /// App Configuration
 #[derive(Parser, Redact)]
@@ -14,10 +14,10 @@ static GLOBAL: MiMalloc = MiMalloc;
 struct Args {
     /// The name of the model to load.
     /// Can be a MODEL_ID as listed on <https://hf.co/models> like
-    /// `thenlper/gte-base`.
+    /// `BAAI/bge-large-en-v1.5`.
     /// Or it can be a local directory containing the necessary files
     /// as saved by `save_pretrained(...)` methods of transformers
-    #[clap(default_value = "thenlper/gte-base", long, env)]
+    #[clap(default_value = "BAAI/bge-large-en-v1.5", long, env)]
     #[redact(partial)]
     model_id: String,
 
@@ -79,6 +79,33 @@ struct Args {
     #[clap(long, env)]
     auto_truncate: bool,
 
+    /// The name of the prompt that should be used by default for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// Must be a key in the `sentence-transformers` configuration `prompts` dictionary.
+    ///
+    /// For example if ``default_prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...},
+    /// then the sentence "What is the capital of France?" will be encoded as
+    /// "query: What is the capital of France?" because the prompt text will be prepended before
+    /// any text to encode.
+    ///
+    /// The argument '--default-prompt-name <DEFAULT_PROMPT_NAME>' cannot be used with
+    /// '--default-prompt <DEFAULT_PROMPT>`
+    #[clap(long, env, conflicts_with = "default_prompt")]
+    default_prompt_name: Option<String>,
+
+    /// The prompt that should be used by default for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// For example if ``default_prompt`` is "query: " then the sentence "What is the capital of
+    /// France?" will be encoded as "query: What is the capital of France?" because the prompt
+    /// text will be prepended before any text to encode.
+    ///
+    /// The argument '--default-prompt <DEFAULT_PROMPT>' cannot be used with
+    /// '--default-prompt-name <DEFAULT_PROMPT_NAME>`
+    #[clap(long, env, conflicts_with = "default_prompt_name")]
+    default_prompt: Option<String>,
+
     /// Your HuggingFace hub token
     #[clap(long, env)]
     #[redact(partial)]
@@ -147,6 +174,20 @@ async fn main() -> Result<()> {
 
     tracing::info!("{args:?}");
 
+    // Hack to trim pages regularly
+    // see: https://www.algolia.com/blog/engineering/when-allocators-are-hoarding-your-precious-memory/
+    // and: https://github.com/huggingface/text-embeddings-inference/issues/156
+    #[cfg(target_os = "linux")]
+    tokio::spawn(async move {
+        use tokio::time::Duration;
+        loop {
+            tokio::time::sleep(Duration::from_millis(100)).await;
+            unsafe {
+                libc::malloc_trim(0);
+            }
+        }
+    });
+
     text_embeddings_router::run(
         args.model_id,
         args.revision,
@@ -158,6 +199,8 @@ async fn main() -> Result<()> {
         args.max_batch_requests,
         args.max_client_batch_size,
         args.auto_truncate,
+        args.default_prompt,
+        args.default_prompt_name,
         args.hf_api_token,
         Some(args.hostname),
         args.port,
diff --git a/router/src/shutdown.rs b/router/src/shutdown.rs
index 471eaf14..15fc8ea7 100644
--- a/router/src/shutdown.rs
+++ b/router/src/shutdown.rs
@@ -25,5 +25,4 @@ pub(crate) async fn shutdown_signal() {
     }
 
     tracing::info!("signal received, starting graceful shutdown");
-    opentelemetry::global::shutdown_tracer_provider();
 }
diff --git a/router/tests/common.rs b/router/tests/common.rs
index c8669c12..55fdf5f5 100644
--- a/router/tests/common.rs
+++ b/router/tests/common.rs
@@ -58,6 +58,8 @@ pub async fn start_server(model_id: String, revision: Option<String>, dtype: DTy
             false,
             None,
             None,
+            None,
+            None,
             8090,
             None,
             None,
diff --git a/router/tests/test_http_predict.rs b/router/tests/test_http_predict.rs
index 6869f721..850e0874 100644
--- a/router/tests/test_http_predict.rs
+++ b/router/tests/test_http_predict.rs
@@ -16,12 +16,13 @@ pub struct SnapshotPrediction {
 #[tokio::test]
 #[cfg(feature = "http")]
 async fn test_predict() -> Result<()> {
-    start_server(
-        "SamLowe/roberta-base-go_emotions".to_string(),
-        None,
-        DType::Float32,
-    )
-    .await?;
+    let model_id = if cfg!(feature = "ort") {
+        "SamLowe/roberta-base-go_emotions-onnx"
+    } else {
+        "SamLowe/roberta-base-go_emotions"
+    };
+
+    start_server(model_id.to_string(), None, DType::Float32).await?;
 
     let request = json!({
         "inputs": "test"
diff --git a/router/tests/test_http_rerank.rs b/router/tests/test_http_rerank.rs
index 0c5b6079..920eca4d 100644
--- a/router/tests/test_http_rerank.rs
+++ b/router/tests/test_http_rerank.rs
@@ -17,12 +17,7 @@ pub struct SnapshotRank {
 #[tokio::test]
 #[cfg(feature = "http")]
 async fn test_rerank() -> Result<()> {
-    start_server(
-        "BAAI/bge-reranker-base".to_string(),
-        Some("refs/pr/5".to_string()),
-        DType::Float32,
-    )
-    .await?;
+    start_server("BAAI/bge-reranker-base".to_string(), None, DType::Float32).await?;
 
     let request = json!({
         "query": "test",