This project is a comprehensive toolkit designed for the full lifecycle management of large language and multimodal models. It functions as a unified orchestrator that handles the entire development process, ranging from dataset preparation and supervised fine-tuning to advanced reinforcement learning alignment and production-ready inference deployment.
The platform distinguishes itself through a specialized reinforcement learning library that supports complex optimization algorithms, including group relative policy optimization and leave-one-out techniques, to improve model instruction-following and safety. It provides extensive support for training stability through sequence-level importance sampling, token-level loss normalization, and uncertainty-based weighting, ensuring reliable policy updates during the alignment phase.
Beyond its core training capabilities, the framework integrates high-performance inference backends and model quantization to facilitate efficient production access. It supports diverse data modalities—including text, image, video, and audio—and offers a modular interface for registering custom model architectures, dialogue templates, and training callbacks. Users can manage these complex workflows through a centralized configuration system or a web-based graphical interface that simplifies task execution and performance monitoring.
