#SFT fine-tuning

The project provides a framework for training a mini Chinese language model from the ground up, focusing on pivotal pre-processing techniques such as data cleaning and tokenizer training. It also highlights flash attention acceleration and offers direction on unsupervised causal language model (CLM) pre-training using the BELLE dataset. Furthermore, it details fine-tuning through supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) to improve model performance. Being experimental with potential major updates, it offers substantial insights into constructing proficient language models, as well as practical examples of conversational abilities and retrieval-augmented generation (RAG).

The project focuses on training a compact 0.2B parameter Chinese generative language model suitable for environments with limited computational resources. The model training is feasible with just 4GB GPU and 16GB RAM, supporting extensive methods like data cleaning, tokenizer training, SFT fine-tuning, and RLHF optimization using open-source datasets. The Huggingface frameworks such as transformers and accelerate assist in the process. Further, the project facilitates uninterrupted training continuation and offers support for downstream task fine-tuning, with regular updates enhancing its utility for researchers in scalable language model implementations.