LLamaRecipes

Llama Recipes: Examples to get started using the Llama models from Meta

Note: We recently did a refactor of the repo, archive-main is a snapshot branch from before the refactor

Welcome to the official repository for helping you get started with inference, fine-tuning and end-to-end use-cases of building with the Llama Model family.

The examples cover the most popular community approaches, popular use-cases and the latest Llama 3.2 Vision and Llama 3.2 Text, in this repository.

[!TIP] Repository Structure:

• Start building with the Llama 3.2 models
• End to End Use cases with Llama model family
• Examples of building with 3rd Party Llama Providers
• Model Benchmarks

[!TIP] Get started with Llama 3.2 with these new recipes:

• Finetune Llama 3.2 Vision
• Multimodal Inference with Llama 3.2 Vision
• Inference on Llama Guard 1B + Multimodal inference on Llama Guard 11B-Vision

[!NOTE] Llama 3.2 follows the same prompt template as Llama 3.1, with a new special token <|image|> representing the input image for the multimodal models.

More details on the prompt templates for image reasoning, tool-calling and code interpreter can be found on the documentation website.

Repository Structure:

• 3P Integrations: Getting Started Recipes and End to End Use-Cases from various Llama providers
• End to End Use Cases: As the name suggests, spanning various domains and applications
• Getting Started: Reference for inferencing, fine-tuning and RAG examples
• Benchmarks: Reference implementation for some benchmarks

FAQ:

• Q: Some links are broken/folders are missing:

A: We recently did a refactor of the repo, archive-main is a snapshot branch from before the refactor

• Where can we find details about the latest models?

A: Official Llama models website

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

PyTorch Nightlies

If you want to use PyTorch nightlies instead of the stable release, go to this guide to retrieve the right --extra-index-url URL parameter for the pip install commands on your platform.

Installing

Llama-recipes provides a pip distribution for easy install and usage in other projects. Alternatively, it can be installed from source.

[!NOTE] Ensure you use the correct CUDA version (from nvidia-smi) when installing the PyTorch wheels. Here we are using 11.8 as cu118. H100 GPUs work better with CUDA >12.0

Install with pip

pip install llama-recipes

Install with optional dependencies

Llama-recipes offers the installation of optional packages. There are three optional dependency groups. To run the unit tests we can install the required dependencies with:

pip install llama-recipes[tests]

For the vLLM example we need additional requirements that can be installed with:

pip install llama-recipes[vllm]

To use the sensitive topics safety checker install with:

pip install llama-recipes[auditnlg]

Some recipes require the presence of langchain. To install the packages follow the recipe description or install with:

pip install llama-recipes[langchain]

Optional dependencies can also be combined with [option1,option2].

Install from source

To install from source e.g. for development use these commands. We're using hatchling as our build backend which requires an up-to-date pip as well as setuptools package.

git clone git@github.com:meta-llama/llama-recipes.git
cd llama-recipes
pip install -U pip setuptools
pip install -e .

For development and contributing to llama-recipes please install all optional dependencies:

git clone git@github.com:meta-llama/llama-recipes.git
cd llama-recipes
pip install -U pip setuptools
pip install -e .[tests,auditnlg,vllm]

Getting the Llama models

You can find Llama models on Hugging Face hub here, where models with hf in the name are already converted to Hugging Face checkpoints so no further conversion is needed. The conversion step below is only for original model weights from Meta that are hosted on Hugging Face model hub as well.

Model conversion to Hugging Face

If you have the model checkpoints downloaded from the Meta website, you can convert it to the Hugging Face format with:

## Install Hugging Face Transformers from source
pip freeze | grep transformers ## verify it is version 4.45.0 or higher

git clone git@github.com:huggingface/transformers.git
cd transformers
pip install protobuf
python src/transformers/models/llama/convert_llama_weights_to_hf.py \
   --input_dir /path/to/downloaded/llama/weights --model_size 3B --output_dir /output/path

Repository Organization

Most of the code dealing with Llama usage is organized across 2 main folders: recipes/ and src/.

recipes/

Contains examples organized in folders by topic: | Subfolder | Description | |---|---| quickstart | The "Hello World" of using Llama, start here if you are new to using Llama. use_cases|Scripts showing common applications of Meta Llama3 3p_integrations|Partner owned folder showing common applications of Meta Llama3 responsible_ai|Scripts to use PurpleLlama for safeguarding model outputs experimental|Meta Llama implementations of experimental LLM techniques

src/

Contains modules which support the example recipes: | Subfolder | Description | |---|---| | configs | Contains the configuration files for PEFT methods, FSDP, Datasets, Weights & Biases experiment tracking. | | datasets | Contains individual scripts for each dataset to download and process. Note | | inference | Includes modules for inference for the fine-tuned models. | | model_checkpointing | Contains FSDP checkpoint handlers. | | policies | Contains FSDP scripts to provide different policies, such as mixed precision, transformer wrapping policy and activation checkpointing along with any precision optimizer (used for running FSDP with pure bf16 mode). | | utils | Utility files for:
- train_utils.py provides training/eval loop and more train utils.
- dataset_utils.py to get preprocessed datasets.
- config_utils.py to override the configs received from CLI.
- fsdp_utils.py provides FSDP wrapping policy for PEFT methods.
- memory_utils.py context manager to track different memory stats in train loop. |

Supported Features

The recipes and modules in this repository support the following features:

| Feature | | | ---------------------------------------------- | - | | HF support for inference | ✅ | | HF support for finetuning | ✅ | | PEFT | ✅ | | Deferred initialization ( meta init) | ✅ | | Low CPU mode for multi GPU | ✅ | | Mixed precision | ✅ | | Single node quantization | ✅ | | Flash attention | ✅ | | Activation checkpointing FSDP | ✅ | | Hybrid Sharded Data Parallel (HSDP) | ✅ | | Dataset packing & padding | ✅ | | BF16 Optimizer (Pure BF16) | ✅ | | Profiling & MFU tracking | ✅ | | Gradient accumulation | ✅ | | CPU offloading | ✅ | | FSDP checkpoint conversion to HF for inference | ✅ | | W&B experiment tracker | ✅ |

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

License

See the License file for Meta Llama 3.2 here and Acceptable Use Policy here

See the License file for Meta Llama 3.1 here and Acceptable Use Policy here

See the License file for Meta Llama 3 here and Acceptable Use Policy here

See the License file for Meta Llama 2 here and Acceptable Use Policy here