Introduction to Lit-LLaMA

Welcome to the world of Lit-LLaMA, an independent implementation of the LLaMA model that focuses on pretraining, finetuning, and inference. This project is fully open-source under the Apache 2.0 license. With a commitment to making AI accessible, Lit-LLaMA aims to provide a robust alternative to the original LLaMA code, which is GPL licensed and restricts integration with other projects.

Key Features of Lit-LLaMA

• Independent Implementation: Built on the foundation of LLaMA and nanoGPT.
• Open Source: Fully open-source, allowing for community contributions and modifications.
• Optimized Performance: Designed to run efficiently on consumer hardware.
• Simple Setup: Easy installation and usage instructions.

Technical Architecture and Implementation

Lit-LLaMA is designed with simplicity and correctness in mind. The implementation is a single-file solution that is numerically equivalent to the original model. It supports various hardware configurations, making it accessible for developers and researchers alike.

To get started, clone the repository:

git clone https://github.com/Lightning-AI/lit-llama
cd lit-llama

Installation Process

After cloning the repository, install the necessary dependencies:

pip install -e ".[all]"

Once the dependencies are installed, you are ready to start using Lit-LLaMA!

Using Lit-LLaMA: Examples and API Overview

To generate text predictions, you will need to download the model weights.

Run inference with the following command:

python generate.py --prompt "Hello, my name is"

This command will utilize the 7B model and requires approximately 26 GB of GPU memory (A100 GPU).

For GPUs with bfloat16 support, the script will automatically convert the weights, consuming about 14 GB. For GPUs with less memory, enable quantization:

python generate.py --quantize llm.int8 --prompt "Hello, my name is"

Finetuning the Model

Lit-LLaMA provides simple training scripts for finetuning the model. You can use the following commands to finetune:

python finetune/lora.py

or

python finetune/adapter.py

Ensure you have downloaded the pretrained weights as described in the setup section.

Community and Contribution

Lit-LLaMA encourages community involvement. You can join our Discord to collaborate on high-performance, open-source models. Contributions are welcome in various areas, including:

• Pre-training
• Fine-tuning (full and LoRA)
• Quantization
• Sparsification

For more information on contributing, check out our Hitchhiker’s Guide.

License and Legal Considerations

Lit-LLaMA is released under the Apache 2.0 license, allowing for broad usage and modification. However, it is important to note that the original LLaMA weights are distributed under a research-only license by Meta.

Conclusion

Lit-LLaMA represents a significant step towards making AI models more accessible and open-source. With its simple setup, optimized performance, and community-driven approach, it is an excellent choice for developers looking to leverage the power of LLaMA.

For more information and to access the repository, visit Lit-LLaMA on GitHub.

FAQ Section