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1. STaR 2. Mesh Transformer JAX 1. Updates 3. Pretrained Models 1. GPT-J-6B 1. Links 2. Acknowledgments 3. License 4. Model Details 5. Zero-Shot Evaluations 4. Architecture and Usage 1. Fine-tuning 2. JAX Dependency 5. TODO
Open-Instruct is a distributed training and instruction tuning framework for large language models. It functions as a coordinator for supervised fine-tuning, reinforcement learning from human feedback pipelines, and tool-use training, providing specialized roles for dataset curation and model alignment. The project distinguishes itself through a high-performance training architecture that utilizes actor-based distributed coordination and hybrid sharding to manage large GPU clusters. It implements advanced alignment techniques including direct preference optimization, group relative policy opt
Skywork-Reward-V2 is a series of eight reward models designed for versatility across a wide range of tasks, trained on a mixture of 26 million carefully curated preference pairs. While the Skywork-Reward-V2 series remains based on the Bradley-Terry model, we push the boundaries of training data…
The main features of skyworkai/skywork-reward-v2 are: Single Agent Optimization.
Projects with overlapping indexed features include: chengpengli1003/cort. chengsong-huang/r-zero — Check out our paper or webpage for the details. ezelikman/star — 1. STaR 2. Mesh Transformer JAX 1. Updates 3. Pretrained Models 1. GPT-J-6B 1. Links 2. Acknowledgments 3. License 4.… hkust-nlp/mstar — :star: Project Page . iamhankai/forest-of-thought — Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning. allenai/open-instruct — Open-Instruct is a distributed training and instruction tuning framework for large language models. It functions as a…
