PKU-Alignment/align-anything
Align Anything
Align-anything is a multi-modal large language model alignment framework designed to fine-tune models across text, image, video, and audio. It functions as a distributed training orchestrator and toolkit for implementing preference-based learning to ensure model behaviors match human intentions and values.
The framework provides specialized pipelines for Supervised Fine-Tuning and Direct Preference Optimization. It includes a high-performance inference engine wrapper for actor models to reduce sequence generation time and a dedicated training environment for refining vision-language-action models used in physical robotics.
The system manages multi-modal data processing via a command-line interface and supports automated deployment of training workloads to resource-managed hardware clusters. Its capabilities cover alignment algorithm implementation, multi-modal fine-tuning, and hardware resource optimization.
Features
- Preference-Based Model Alignments - Improves model outputs across image, video, and audio data using human feedback and preference-based learning.
- Preference Alignment - Implements supervised fine-tuning and direct preference optimization to align multi-modal model outputs with human feedback.
- Alignment Frameworks - Provides an integrated toolkit for end-to-end supervised fine-tuning and preference optimization across multiple modalities.
- Vision-Language-Action Training - Provides training workflows for vision-language-action models used in physical robotics to ensure operational reliability.
- Multi-Modal Data Processing - Includes a command-line interface to manage and streamline the processing of diverse media inputs before training.
- Multi-Modal Training - Provides a unified training interface that processes image, audio, and video data for consistent multi-modal fine-tuning.
- Model Alignment and Feedback - Implements techniques for aligning model behaviors with human intentions and values using feedback and reinforcement learning.
- Multi-Modal Fine-Tunings - Allows training models using supervised learning and preference alignment to improve performance across image, video, and audio data.
- Preference-Based Alignment Pipelines - Provides specialized pipelines for Direct Preference Optimization and Supervised Fine-Tuning to align model behaviors.
- LLM Training Orchestrators - Functions as a management layer for deploying alignment workloads to clusters with automated hardware allocation.
- Training Acceleration Engines - Wraps actor models in a high-performance inference engine to accelerate sequence generation during the training process.
- Multi-Modal Training Accelerators - Accelerates training times and manages hardware resources for models processing complex multi-modal media sequences.
- Automated Workload Deployments - Provides automated scripts to deploy training workloads across resource-managed hardware clusters for optimized compute usage.
- Slurm Cluster Orchestrations - Supports moving training workloads to resource-managed Slurm clusters using automated allocation scripts.
- Inference Engine Integrations - Integrates actor models with high-performance inference engines to reduce sequence generation time during training.
- Language Model Development - Framework for aligning various modality models with human values.
- RLHF Frameworks - Unified framework for multi-modality instruction following.
