This library provides a comprehensive framework for fine-tuning, aligning, and distilling transformer-based language models. It serves as a toolkit for adapting models to specialized domains through supervised learning, while offering advanced methodologies to improve output quality and reasoning capabilities.
The project distinguishes itself through specialized alignment and optimization techniques, including direct preference optimization and reinforcement learning, which allow models to be tuned against human preferences without complex reward modeling. It further supports training efficiency through asynchronous rollout decoupling, which separates generation from gradient updates, and improves convergence stability by utilizing bias-corrected moving averages for model weights.
Beyond core training, the library includes utilities for knowledge distillation to transfer capabilities from large teacher models to smaller architectures. It also provides integrated tools for monitoring training progress, logging model completions, and tracking evaluation traces to support performance analysis throughout the development lifecycle.
