Generate synthetic data for your AI models with SDG Hub
A tutorial on using SDG Hub to turn a small amount of quality data into larger useful datasets through automated pipelines that generate and validate synthetic data.
Blog
Building domain-specific LLMs with synthetic data and SDG Hub
How synthetic data generation and SDG Hub enable organizations to efficiently create domain-specific language models using techniques like submodular optimization.
Get started with language model post-training using Training Hub
Introducing Training Hub: An open source, algorithm-centered library for LLM training.
Post Training Methods Language Models
Post-training adapts language models for specific, safe, and practical uses. This overview highlights key methods and the open source training_hub library.
SDG Hub: Building synthetic data pipelines with modular blocks
SDG Hub is an open framework for building, composing, and scaling synthetic data pipelines with modular blocks for LLM training.
Getting reasoning models enterprise ready
Customize reasoning models with synthetic data generation for enterprise deployment. Learn techniques from Red Hat's AI Innovation Team.
Beyond tokens per second: Unlocking smarter enterprise AI with inference-time scaling
Discover inference-time scaling techniques that improve AI quality and reliability for enterprise applications beyond just speed optimization.
Async-GRPO - Open, Fast, and Performant
Introducing Async-GRPO - an open-source library for scalable reinforcement learning with 42% efficiency gains over VERL and 11x over TRL for GRPO training.
Sculpting Subspaces: How We Solved Continual Learning in Large Language Models
Learn how our adaptive SVD method enables continual learning in LLMs with near-zero catastrophic forgetting, achieving 7% higher accuracy than baselines.