Get started with language model post-training using Training Hub
Introducing Training Hub: An open source, algorithm-centered library for LLM training.
Open-source AI
A collective of researchers and engineers from Red Hat & IBM building LLM toolkits you can use today.
LLM Hubs
its_hub
Inference-time scaling for LLMs.
sdg_hub
Synthetic data generation pipelines
training_hub
Post training algorithms for LLMs
LLM Tools
async-grpo
Asynchronous GRPO for scalable reinforcement learning.
hopscotch
A method for skipping redundant attention blocks in language models
mini_trainer
Efficient training library for large language models up to 70B parameters on a single node.
orthogonal-subspace-learning
Adaptive SVD-based continual learning method for LLMs.
probabilistic-inference-scaling
Inference-time scaling with particle filtering.
reward_hub
State-of-the-art reward models for preference data generation and acceptance criteria.
SQuat
KV cache quantization for scaling inference time
training
Efficient messages-format SFT library for language models
Recent posts
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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.
Getting Reasoning Models Enterprise Ready
Customize reasoning models with synthetic data generation for enterprise deployment. Learn techniques from Red Hat's AI Innovation Team.
Recent videos
View all videosRandom Samples
📹 Nondeterminism in LLM Inference & Training-Rollout Mismatch
👤 Speaker: Xinheng Ding
Random Samples
📹 Evolutionary Arms Races Between LLMs in Core War
👤 Speaker: Akarsh Kumar
Random Samples
📹 Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
👤 Speaker: Qizheng Zhang