OpenChatKit is a training and inference toolkit for large language models. It provides a comprehensive set of tools for managing the model lifecycle, including a fine-tuning pipeline, a model weight converter, and a command-line interface for interacting with conversational agents.
The toolkit features a framework for retrieval augmented generation, allowing models to incorporate relevant context from external vector indices. It also includes utilities for converting trained model checkpoints into formats compatible with standard inference libraries.
The project covers conversational AI training through instruction-tuning and context window optimization, supported by 8-bit quantized optimization to reduce memory overhead. It provides capabilities for stateful conversation tracking, metric-based training logging to monitor convergence, and shell-based model testing to evaluate hyperparameters and response quality.
