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PKU-Alignment/align-anything

A modular framework for aligning any-modality large models with human intentions and values using various fine-tuning and reinforcement learning methods.

Core Features

Highly modular framework for easy customization and modification.

Supports fine-tuning of diverse multi-modal models (image/video/audio).

Implements various alignment algorithms including SFT, DPO, PPO, and Rule-based RL.

Provides a multi-modal command-line interface (CLI).

Accelerates training processes with vLLM engine integration.

Detailed Introduction

Align-Anything is an open-source, highly modular framework developed by the PKU-Alignment Team, designed to align large models across all modalities (image, video, audio, text) with human intentions and values. It offers a comprehensive suite of fine-tuning algorithms like SFT, DPO, PPO, and rule-based reinforcement learning, enabling researchers and developers to customize and train diverse multi-modal models. The project emphasizes flexibility, performance acceleration (e.g., with vLLM), and provides tools for large-scale evaluation, making it a robust platform for advanced AI alignment research and development.