#instruction fine-tuning

Based on the latest Meta Llama-3 model, this project provides an open-source platform for crafting high-efficiency Chinese language models. The releases feature the Llama-3-Chinese base and Llama-3-Chinese-Instruct models, extensively fine-tuned on Chinese dataset, boosting semantic and instruction processing. Key enhancements include a larger vocabulary, extended context length, and efficient group query attention. Users have the flexibility to train or fine-tune with provided scripts compatible with popular frameworks like transformers. Discover next-level Chinese NLP with these cutting-edge models.

The project collaborates with the Alpaca-CoT platform to enhance language models' understanding of tabular data. With a focus on organizing diverse, open-source datasets for table-related tasks, such as table QA and table-to-text generation, it transforms these datasets into an instruction tuning format. This approach refines LLMs to improve their ability in handling tables, assisting researchers in developing domain-specific models. By supporting the open-source community, the initiative aims to regenerate models with better table processing capabilities, providing expanded data and model resources.

This project offers access to enhanced Chinese LLaMA and Alpaca models, designed to improve language understanding and execution of instructional tasks. By integrating additional Chinese vocabulary, these models enhance semantic comprehension and decoding efficiency over the original LLaMA. Instruction tuning further refines Alpaca models' performance. The project includes pre-training tools and supports various platforms such as 🤗transformers and LlamaChat, facilitating seamless integration and deployment on personal systems. A range of models, including 7B, 13B, and 33B versions, are available to address diverse requirements.