Llama Chinese is the largest open-source community and model project dedicated to Meta’s Llama family for Chinese-language use. Run by the FlagAlpha team, it brings together Chinese pretrained and fine-tuned Llama models, training code, data resources, and step-by-step tutorials in one place. The GitHub repository has gathered more than 14,000 stars, and the community continues to track each new Llama release.
The project’s own contribution is the Atom series: open Chinese pretrained models built on Llama and released for commercial use, distributed through Hugging Face, ModelScope, and WiseModel. Around those models, the repository curates official Llama releases and Chinese fine-tuned variants across generations, from Llama 2 through Llama 4, so developers can find a suitable base quickly. What really sets the project apart is its documentation: complete Chinese-language guides walk through environment setup, running Llama with Anaconda, Docker, llama.cpp, or Ollama, building API services, pretraining, LoRA and full-parameter fine-tuning, quantization, and deployment acceleration with engines such as vLLM, TensorRT-LLM, and LMDeploy. Community sections organize compute resources, datasets, forums, and applications, making the repo a hub rather than just a code dump.
Llama Chinese is a free community project. Its models and guides are open to everyone, and the Atom models are released for commercial use. Underlying Llama models remain subject to Meta’s own license terms.
The project is ideal for Chinese-speaking developers and researchers who want to build on Llama: fine-tuning a Chinese assistant, learning LLM training workflows, or finding a ready Chinese base model. Beginners get a guided path from setup to deployment, while experienced teams get curated models and battle-tested scripts.