The 'llama-recipes' repository is a companion to the Meta Llama models. We support the latest version, Llama 3.1, in this repository. The goal is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama and other tools in the LLM ecosystem. The examples here showcase how to run Llama locally, in the cloud, and on-prem.
Important
Meta Llama 3.1 has a new prompt template and special tokens.
| Token | Description |
|---|---|
<|begin_of_text|> |
Specifies the start of the prompt. |
<|eot_id|> |
This token signifies the end of a turn i.e. the end of the model's interaction either with the user or tool executor. |
<|eom_id|> |
End of Message. A message represents a possible stopping point where the model can inform the execution environment that a tool call needs to be made. |
<|python_tag|> |
A special tag used in the model’s response to signify a tool call. |
<|finetune_right_pad_id|> |
Used for padding text sequences in a batch to the same length. |
<|start_header_id|>{role}<|end_header_id|> |
These tokens enclose the role for a particular message. The possible roles can be: system, user, assistant and ipython. |
<|end_of_text|> |
This is equivalent to the EOS token. For multiturn-conversations it's usually unused, this token is expected to be generated only by the base models. |
A multiturn-conversation with Meta Llama 3.1 that includes tool-calling follows this structure:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
<|python_tag|>{{ model_tool_call_1 }}<|eom_id|><|start_header_id|>ipython<|end_header_id|>
{{ tool_response }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{model_response_based_on_tool_response}}<|eot_id|>
Each message gets trailed by an <|eot_id|> token before a new header is started, signaling a role change.
More details on the new tokenizer and prompt template can be found here.
Note
The llama-recipes repository was recently refactored to promote a better developer experience of using the examples. Some files have been moved to new locations. The src/ folder has NOT been modified, so the functionality of this repo and package is not impacted.
Make sure you update your local clone by running git pull origin main
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.
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.
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
pip install llama-recipes
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]
Optional dependencies can also be combines with [option1,option2].
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 [email protected]: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 [email protected]:meta-llama/llama-recipes.git
cd llama-recipes
pip install -U pip setuptools
pip install -e .[tests,auditnlg,vllm]
You can find Meta 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.
The recipes and notebooks in this folder are using the Meta Llama model definition provided by Hugging Face's transformers library.
Given that the original checkpoint resides under models/7B you can install all requirements and convert the checkpoint with:
## Install Hugging Face Transformers from source
pip freeze | grep transformers ## verify it is version 4.31.0 or higher
git clone [email protected]: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 7B --output_dir /output/pathMost of the code dealing with Llama usage is organized across 2 main folders: recipes/ and src/.
Contains examples are 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 |
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. |
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 | ✅ |
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
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