This repository serves as a comprehensive hub for the Chinese Llama community, aiming to foster an open-source ecosystem for Llama large language models. It provides resources, tools, and community support for developers and enthusiasts focused on Chinese language optimization and applications of Llama models.

How It Works

The project aggregates and shares the latest Llama learning materials, including official model releases (Llama 2, 3, and 4), community-finetuned Chinese models (like Atom), and fine-tuning scripts. It emphasizes practical application through quick-start guides for various deployment methods (Anaconda, Docker, llama.cpp, Gradio, API services, Ollama) and offers detailed instructions for model pre-training, fine-tuning (LoRA and full parameter), quantization, and deployment acceleration using frameworks like TensorRT-LLM and vLLM.

Quick Start & Requirements

• Installation: Clone the repository and install dependencies via pip install -r requirements.txt.
• Prerequisites: Python 3.10+ is recommended. GPU acceleration is highly beneficial for training and inference.
• Resources: Detailed guides for using Anaconda, Docker, llama.cpp, Gradio, building API services with FastChat, and running with Ollama are provided. Links to Hugging Face, ModelScope, and WiseModel for model downloads are included.

Highlighted Details

• Offers community-developed Chinese-optimized Llama models (e.g., Atom series) with extensive Chinese data pre-training and optimized tokenizers.
• Provides comprehensive fine-tuning scripts for both LoRA and full parameter tuning, along with code for loading and merging LoRA weights.
• Includes support for various inference acceleration frameworks like TensorRT-LLM, vLLM, JittorLLMs, and lmdeploy.
• Features integration with LangChain for building LLM-powered applications and tools.

Maintenance & Community

The community is active, with regular updates on new model releases and features. They encourage community contributions and provide channels for discussion and support, including a forum and links to WeChat groups.

Licensing & Compatibility

The project states its models are "completely open-source and commercially usable." Specific model licenses should be verified on their respective Hugging Face pages.

Limitations & Caveats

While the project focuses on Chinese optimization, the performance of base Llama models on Chinese tasks without fine-tuning is noted as generally weak, often producing mixed-language or irrelevant responses. The community aims to address this through ongoing development and data contributions.