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A large-scale, fine-grained, diverse preference dataset
The main features of thunlp/ultrafeedback are: Feedback Alignment, Model Alignment and Feedback, Reinforcement Learning.
Projects with overlapping indexed features include: facebookresearch/motif — This repository contains PyTorch code for Motif, training AI agents on NetHack with reward functions derived from an… volcengine/verl — verl is a distributed training system designed for large language model alignment and reinforcement learning. It… rucaibox/rlmec — This repo provides the source code & data of our paper: Improving Large Language Models via Fine-grained Reinforcement… pku-alignment/align-anything — Align-anything is a multi-modal large language model alignment framework designed to fine-tune models across text,… microsoft/lmops — LMOps is a research-driven operations framework for optimizing the deployment, fine-tuning, and performance of large… optimalscale/lmflow — LMFlow is a comprehensive suite for large language model fine-tuning, context extension, multimodal processing, and…
This repository contains PyTorch code for Motif, training AI agents on NetHack with reward functions derived from an LLM's preferences.
verl is a distributed training system designed for large language model alignment and reinforcement learning. It provides a framework for executing post-training pipelines, including supervised fine-tuning and reinforcement learning from human feedback, to refine model behavior and agentic capabilities. The system utilizes a hybrid training and inference engine that optimizes memory and communication when switching between model generation and gradient updates. It supports multi-modal reinforcement learning for models processing both image and text data, and implements algorithms such as PPO
This repo provides the source code & data of our paper: Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint (arXiv 2024)
LMFlow is a comprehensive suite for large language model fine-tuning, context extension, multimodal processing, and inference execution. It provides a toolkit for updating model parameters through full tuning or memory-efficient adapter algorithms, alongside an inference engine for executing tuned models via command-line or web-based interfaces. The framework includes a dedicated alignment suite for supervised tuning and reward model training to refine model behavior. It features a context window extender to increase maximum input lengths and a multimodal framework for building chatbots that