This project is a collection of scripts and workflows for training, fine-tuning, and deploying large language models using the Hugging Face Transformers toolkit. It functions as a distributed training framework, a library for natural language processing task implementations, and a system for building retrieval-augmented generation chatbots.
The repository includes specialized tools for model optimization, such as a Bayesian hyperparameter optimizer for automatically tuning model settings. It provides implementations for scaling model training across multiple graphics processors using data parallelism and low-precision quantization.
The library covers a wide range of natural language processing capabilities, including text summarization, question answering, token classification, and sentence similarity measurement. It also supports the development of generative and retrieval-based conversational agents.
The project is implemented primarily using Jupyter Notebooks.