llama README

The project's README from github.com/meta-llama/llama. As one markdown file: README.md.

Note of deprecation

Thank you for developing with Llama models. As part of the Llama 3.1 release, we’ve consolidated GitHub repos and added some additional repos as we’ve expanded Llama’s functionality into being an e2e Llama Stack. Please use the following repos going forward:

If you have any questions, please feel free to file an issue on any of the above repos and we will do our best to respond in a timely manner.

Thank you!

(Deprecated) Llama 2

We are unlocking the power of large language models. Llama 2 is now accessible to individuals, creators, researchers, and businesses of all sizes so that they can experiment, innovate, and scale their ideas responsibly.

This release includes model weights and starting code for pre-trained and fine-tuned Llama language models — ranging from 7B to 70B parameters.

This repository is intended as a minimal example to load Llama 2 models and run inference. For more detailed examples leveraging Hugging Face, see llama-cookbook.

Updates post-launch

See UPDATES.md. Also for a running list of frequently asked questions, see here.

Download

In order to download the model weights and tokenizer, please visit the Meta website and accept our License.

Once your request is approved, you will receive a signed URL over email. Then run the download.sh script, passing the URL provided when prompted to start the download.

Pre-requisites: Make sure you have wget and md5sum installed. Then run the script: ./download.sh.

Keep in mind that the links expire after 24 hours and a certain amount of downloads. If you start seeing errors such as 403: Forbidden, you can always re-request a link.

Access to Hugging Face

We are also providing downloads on Hugging Face. You can request access to the models by acknowledging the license and filling the form in the model card of a repo. After doing so, you should get access to all the Llama models of a version (Code Llama, Llama 2, or Llama Guard) within 1 hour.

Quick Start

You can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the Llama 2 cookbook repository.

  1. In a conda env with PyTorch / CUDA available clone and download this repository.

  2. In the top-level directory run:

    pip install -e .

  3. Visit the Meta website and register to download the model/s.

  4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the download.sh script.

  5. Once you get the email, navigate to your downloaded llama repository and run the download.sh script.

    • Make sure to grant execution permissions to the download.sh script
    • During this process, you will be prompted to enter the URL from the email.
    • Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.

  6. Once the model/s you want have been downloaded, you can run the model locally using the command below:

torchrun --nproc_per_node 1 example_chat_completion.py \
    --ckpt_dir llama-2-7b-chat/ \
    --tokenizer_path tokenizer.model \
    --max_seq_len 512 --max_batch_size 6

Note

Inference

Different models require different model-parallel (MP) values:

Model MP
7B 1
13B 2
70B 8

All models support sequence length up to 4096 tokens, but we pre-allocate the cache according to max_seq_len and max_batch_size values. So set those according to your hardware.

Pretrained Models

These models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.

See example_text_completion.py for some examples. To illustrate, see the command below to run it with the llama-2-7b model (nproc_per_node needs to be set to the MP value):

torchrun --nproc_per_node 1 example_text_completion.py \
    --ckpt_dir llama-2-7b/ \
    --tokenizer_path tokenizer.model \
    --max_seq_len 128 --max_batch_size 4

Fine-tuned Chat Models

The fine-tuned models were trained for dialogue applications. To get the expected features and performance for them, a specific formatting defined in chat_completion needs to be followed, including the INST and <<SYS>> tags, BOS and EOS tokens, and the whitespaces and breaklines in between (we recommend calling strip() on inputs to avoid double-spaces).

You can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-cookbook repo for an example of how to add a safety checker to the inputs and outputs of your inference code.

Examples using llama-2-7b-chat:

torchrun --nproc_per_node 1 example_chat_completion.py \
    --ckpt_dir llama-2-7b-chat/ \
    --tokenizer_path tokenizer.model \
    --max_seq_len 512 --max_batch_size 6

Llama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios. In order to help developers address these risks, we have created the Responsible Use Guide. More details can be found in our research paper as well.

Issues

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

Model Card

See MODEL_CARD.md.

License

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

See the LICENSE file, as well as our accompanying Acceptable Use Policy

References

  1. Research Paper
  2. Llama 2 technical overview
  3. Open Innovation AI Research Community

For common questions, the FAQ can be found here which will be kept up to date over time as new questions arise.

Original Llama

The repo for the original llama release is in the llama_v1 branch.

llama (meta-llama/llama)

language-model
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59.6K ·GitHub
Llama
What is this?This is Meta's reference inference code for running Llama 2 language models (7B to 70B parameters) locally, letting you load pre-trained or chat fine-tuned weights and generate text completions.
Is this a full app or just example code?It is a minimal example codebase, not a packaged app or server — you clone the repo, load the weights yourself, and call Python scripts like example_chat_completion.py directly.
How do I get the models running?Set up a conda environment with PyTorch and CUDA, request model access on the Meta website, then run './download.sh' with the signed URL emailed to you, and launch inference with torchrun pointing at the checkpoint directory and tokenizer.
What do I need before I can run it?A conda environment with PyTorch and CUDA, plus 'wget' and 'md5sum' for the download script, and enough GPUs to match the model's model-parallel requirement (1/2/8 GPUs for 7B/13B/70B).
Is this repo still the right place to use Llama?No — this repo is deprecated; Meta moved ongoing development to llama-models, llama-toolchain, llama-agentic-system and llama-cookbook, so this code is now just a legacy minimal example.
Can I use the weights commercially?Yes, Meta licenses the model and weights for both researchers and commercial entities, subject to the LICENSE file and an Acceptable Use Policy.
Do I need a specific amount of GPU hardware?Yes — different model sizes require different model-parallel (MP) GPU counts: 1 GPU for 7B, 2 for 13B, and 8 for the 70B model.
What's the difference between the pretrained and chat models?Pretrained models only continue text naturally and aren't tuned for dialogue, while fine-tuned chat models expect a specific INST/BOS/EOS prompt format for conversational use.
How long a context can these models handle?All models support sequence lengths up to 4096 tokens, but you must set max_seq_len and max_batch_size according to your hardware's memory.
Can I get the weights from Hugging Face instead of download.sh?Yes, after accepting the license on the Meta website you can also request access via the Hugging Face model cards and typically get access within an hour.
What should I use instead of this repo now?Meta points users to llama-models for weights/model cards, llama-toolchain for fine-tuning and safety, llama-agentic-system for agentic apps, and llama-cookbook for community examples.
What are similar projects?jeeves, ai-torture-chamber, airllm, fairseq, open-r1, petals, ColossalAI, decider
Which topics is it in?model, foundation-models, language-model, llama, large-language-models, nlp, artificial-intelligence, inference, nvidia, deprecated, huggingface, cuda
Creator
meta-llama
Size
1.1 MB
Stars
59.6K
Updated
CommitJan 26
Age
3y
Commits · 90 Days