MOSS is a conversational AI API server and framework designed to manage stateful multi-turn dialogues via session identifiers for remote interaction. It functions as a tool-augmented language model framework and a quantized inference engine.
The project integrates external plugins, such as search engines and calculators, to provide factual and computed data within model responses. It also includes a supervised fine-tuning toolkit for adapting base language models to specific conversational datasets and behavioral instructions.
The system supports inference optimization through 4-bit and 8-bit weight quantization to reduce GPU memory and computation costs. It further provides capabilities for model API hosting and the deployment of interactive demos via web or command-line interfaces.
