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Llama Models

Llama is an accessible, open large language model (LLM) designed for developers, researchers, and businesses to build, experiment, and responsibly scale their generative AI ideas. Part of a foundational system, it serves as a bedrock for innovation in the global community. A few key aspects:

1. Open access: Easy accessibility to cutting-edge large language models, fostering collaboration and advancements among developers, researchers, and organizations
2. Broad ecosystem: Llama models have been downloaded hundreds of millions of times, there are thousands of community projects built on Llama and platform support is broad from cloud providers to startups - the world is building with Llama!
3. Trust & safety: Llama models are part of a comprehensive approach to trust and safety, releasing models and tools that are designed to enable community collaboration and encourage the standardization of the development and usage of trust and safety tools for generative AI

Our mission is to empower individuals and industry through this opportunity while fostering an environment of discovery and ethical AI advancements. The model weights are licensed for researchers and commercial entities, upholding the principles of openness.

Llama Models

Model	Launch date	Model sizes	Context Length	Tokenizer	Acceptable use policy	License	Model Card
Llama 2	7/18/2023	7B, 13B, 70B	4K	Sentencepiece	Use Policy	License	Model Card
Llama 3	4/18/2024	8B, 70B	8K	TikToken-based	Use Policy	License	Model Card
Llama 3.1	7/23/2024	8B, 70B, 405B	128K	TikToken-based	Use Policy	License	Model Card
Llama 3.2	9/25/2024	1B, 3B	128K	TikToken-based	Use Policy	License	Model Card
Llama 3.2-Vision	9/25/2024	11B, 90B	128K	TikToken-based	Use Policy	License	Model Card
Llama 3.3	12/04/2024	11B, 90B	128K	TikToken-based	Use Policy	License	Model Card
Llama 4	4/5/2025	Scout-17B-16E, Maverick-17B-128E	10M, 1M	TikToken-based	Use Policy	License	Model Card

Download

To download the model weights and tokenizer:

1. Visit the Meta Llama website.

2. Read and accept the license.

3. Once your request is approved you will receive a signed URL via email.

4. Install the Llama CLI: pip install llama-stack. (<-- Start Here if you have received an email already.)

5. Run llama model list to show the latest available models and determine the model ID you wish to download. NOTE: If you want older versions of models, run llama model list --show-all to show all the available Llama models.

6. Run: llama download --source meta --model-id CHOSEN_MODEL_ID

7. Pass the URL provided when prompted to start the download.

Remember that the links expire after 24 hours and a certain amount of downloads. You can always re-request a link if you start seeing errors such as 403: Forbidden.

Running the models

In order to run the models, you will need to install dependencies after checking out the repository.

# Run this within a suitable Python environment (uv, conda, or virtualenv)
pip install .[torch]

Example scripts are available in models/{ llama3, llama4 }/scripts/ sub-directory. Note that the Llama4 series of models require at least 4 GPUs to run inference at full (bf16) precision.

#!/bin/bash

NGPUS=4
CHECKPOINT_DIR=~/.llama/checkpoints/Llama-4-Scout-17B-16E-Instruct
PYTHONPATH=$(git rev-parse --show-toplevel) \
  torchrun --nproc_per_node=$NGPUS \
  -m models.llama4.scripts.chat_completion $CHECKPOINT_DIR \
  --world_size $NGPUS

The above script should be used with an Instruct (Chat) model. For a Base model, update the CHECKPOINT_DIR path and use the script models.llama4.scripts.completion.

Running inference with FP8 and Int4 Quantization

You can reduce the memory footprint of the models at the cost of minimal loss in accuracy by running inference with FP8 or Int4 quantization. Use the --quantization-mode flag to specify the quantization mode. There are two modes:

• fp8_mixed: Mixed precision inference with FP8 for some weights and bfloat16 for activations.
• int4_mixed: Mixed precision inference with Int4 for some weights and bfloat16 for activations.

Using FP8, running Llama-4-Scout-17B-16E-Instruct requires 2 GPUs with 80GB of memory. Using Int4, you need a single GPU with 80GB of memory.

MODE=fp8_mixed  # or int4_mixed
if [ $MODE == "fp8_mixed" ]; then
  NGPUS=2
else
  NGPUS=1
fi
CHECKPOINT_DIR=~/.llama/checkpoints/Llama-4-Scout-17B-16E-Instruct
PYTHONPATH=$(git rev-parse --show-toplevel) \
  torchrun --nproc_per_node=$NGPUS \
  -m models.llama4.scripts.chat_completion $CHECKPOINT_DIR \
  --world_size $NGPUS \
  --quantization-mode $MODE

For more flexibility in running inference (including using other providers), please see the Llama Stack toolset.

Access to Hugging Face

We also provide downloads on Hugging Face, in both transformers and native llama4 formats. To download the weights from Hugging Face, please follow these steps:

• Visit one of the repos, for example meta-llama/Llama-4-Scout-17B-16E.
• Read and accept the license. Once your request is approved, you'll be granted access to all Llama 3.1 models as well as previous versions. Note that requests used to take up to one hour to get processed.
• To download the original native weights to use with this repo, click on the "Files and versions" tab and download the contents of the original folder. You can also download them from the command line if you pip install huggingface-hub:

huggingface-cli download meta-llama/Llama-4-Scout-17B-16E-Instruct-Original --local-dir meta-llama/Llama-4-Scout-17B-16E-Instruct-Original

• To use with transformers, the following snippet will download and cache the weights:

  # inference.py
  from transformers import AutoTokenizer, Llama4ForConditionalGeneration
  import torch
  
  model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
  
  tokenizer = AutoTokenizer.from_pretrained(model_id)
  
  messages = [
      {"role": "user", "content": "Who are you?"},
  ]
  inputs = tokenizer.apply_chat_template(
      messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
  )
  
  model = Llama4ForConditionalGeneration.from_pretrained(
      model_id, device_map="auto", torch_dtype=torch.bfloat16
  )
  
  outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
  outputs = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[-1] :])
  print(outputs[0])

   torchrun --nnodes=1 --nproc_per_node=8 inference.py

Installations

You can install this repository as a package by just doing pip install llama-models

Responsible Use

Llama models are a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios. To help developers address these risks, we have created the Responsible Use Guide.

Issues

Please report any software “bug” or other problems with the models through one of the following means:

• Reporting issues with the model: https://github.com/meta-llama/llama-models/issues
• Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
• Reporting bugs and security concerns: facebook.com/whitehat/info

Questions

For common questions, the FAQ can be found here, which will be updated over time as new questions arise.