This repository provides a straightforward Python interface for running and training Meta's LLaMA large language models using PyTorch and Hugging Face. It targets users who want to easily leverage LLaMA's capabilities without complex setup or custom transformer forks, offering a simplified path to local LLM experimentation.

How It Works

The project leverages the Hugging Face transformers library to load and utilize pre-trained LLaMA models. It abstracts away much of the boilerplate code typically associated with model loading, tokenization, and generation, presenting a clean, single-file interface for direct use. This approach prioritizes simplicity and ease of integration for users familiar with Python and PyTorch.

Quick Start & Requirements

• Primary install: git clone https://github.com/ypeleg/llama
• Prerequisites: PyTorch, Hugging Face transformers, Python. Requires access to LLaMA model weights (e.g., decapoda-research/llama-7b-hf). GPU recommended for performance.
• Links: GitHub Repo

Highlighted Details

• Supports multiple LLaMA model sizes (7B, 13B, 30B, 65B parameters).
• Provides a minimal, single-file Python interface for core LLM operations.
• Demonstrates straightforward generation and decoding of text.

Maintenance & Community

• Code is based on a contribution by Jason Phang.
• No explicit community channels or roadmap are mentioned in the README.

Licensing & Compatibility

• The README does not explicitly state a license. The project's use of LLaMA weights is subject to Meta's original release terms, which were for research purposes.

Limitations & Caveats

The project relies on external LLaMA model weights, which may have licensing restrictions. The README does not specify compatibility with non-NVIDIA GPUs or provide detailed performance benchmarks.