+ converting2seq_cls_models.py

Author: noooop

examples/offline_inference/converting2seq_cls_models.py

@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa: E501
+import argparse
+import json
+
+import torch
+import transformers
+
+# Usage:
+# for Qwen3-Reranker
+# python converting.py --model_name Qwen/Qwen3-Reranker-0.6B --classifier_from_tokens '["no", "yes"]' --method from_2_way_softmax --path ./Qwen3-Reranker-0.6B-seq-cls
+# for BAAI/bge-reranker-v2-gemma
+# python converting.py --model_name BAAI/bge-reranker-v2-gemma --classifier_from_tokens '["yes"]' --method no_post_processing --path ./bge-reranker-v2-gemma-seq-cls
+
+
+def from_2_way_softmax(
+    causal_lm, seq_cls_model, tokenizer, classifier_from_tokens, device
+):
+    # for Qwen3-Reranker
+    # Adapted from https://huggingface.co/Qwen/Qwen3-Reranker-0.6B/discussions/3
+    assert len(classifier_from_tokens) == 2
+
+    lm_head_weights = causal_lm.lm_head.weight
+
+    a = tokenizer.convert_tokens_to_ids(classifier_from_tokens[0])
+    b = tokenizer.convert_tokens_to_ids(classifier_from_tokens[1])
+
+    score_weight = lm_head_weights[b].to(torch.float32).to(device).to(
+        torch.float32
+    ) - lm_head_weights[a].to(device)
+
+    with torch.no_grad():
+        seq_cls_model.score.weight.copy_(score_weight.unsqueeze(0))
+        if seq_cls_model.score.bias is not None:
+            seq_cls_model.score.bias.zero_()
+
+
+def no_post_processing(
+    causal_lm, seq_cls_model, tokenizer, classifier_from_tokens, device
+):
+    # for BAAI/bge-reranker-v2-gemma
+
+    lm_head_weights = causal_lm.lm_head.weight
+    tokens = [tokenizer.convert_tokens_to_ids(t) for t in classifier_from_tokens]
+    score_weight = lm_head_weights[tokens].to(device)
+
+    with torch.no_grad():
+        seq_cls_model.score.weight.copy_(score_weight)
+        if seq_cls_model.score.bias is not None:
+            seq_cls_model.score.bias.zero_()
+
+
+method_map = {
+    function.__name__: function for function in [from_2_way_softmax, no_post_processing]
+}
+
+
+def converting(model_name, classifier_from_tokens, path, method, device="cpu"):
+    assert method in method_map
+
+    tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+    causal_lm = transformers.AutoModelForCausalLM.from_pretrained(
+        model_name, device_map=device
+    )
+
+    seq_cls_model = transformers.AutoModelForSequenceClassification.from_pretrained(
+        model_name, num_labels=1, ignore_mismatched_sizes=True, device_map=device
+    )
+
+    method_map[method](
+        causal_lm, seq_cls_model, tokenizer, classifier_from_tokens, device
+    )
+
+    seq_cls_model.config.pad_token_id = tokenizer.pad_token_id
+
+    seq_cls_model.save_pretrained(path)
+    tokenizer.save_pretrained(path)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Converting *ForCausalLM models to "
+        "*ForSequenceClassification models."
+    )
+    parser.add_argument(
+        "--model_name", type=str, default="Qwen/Qwen3-Reranker-0.6B", help="Model name"
+    )
+    parser.add_argument(
+        "--classifier_from_tokens",
+        type=str,
+        default='["no", "yes"]',
+        help="classifier from tokens",
+    )
+    parser.add_argument(
+        "--method", type=str, default="from_2_way_softmax", help="Converting converting"
+    )
+    parser.add_argument(
+        "--path",
+        type=str,
+        default="./converted_model",
+        help="Path to save converted model",
+    )
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    converting(
+        model_name=args.model_name,
+        classifier_from_tokens=json.loads(args.classifier_from_tokens),
+        method=args.method,
+        path=args.path,
+    )

examples/offline_inference/qwen3_reranker.py

@@ -19,6 +19,11 @@
 # concise, for example.
 # model = LLM(model="tomaarsen/Qwen3-Reranker-0.6B-seq-cls", task="score")
 
+# Offline conversion from official original version to sequence classification
+# model code please refer to: converting2seq_cls_models.py
+# The init parameters are as follows.
+# model = LLM(model="path_to/converted_model", task="score")
+
 # If you want to load the official original version, the init parameters are
 # as follows.