SpecBench: Turning Intent into Specifications
SpecBench benchmarks how well AI coding agents collaborate with users to turn vague ideas into structured specifications, and introduces Buddy, an agent that drafts better specs with fewer questions.
Blog
Unsloth and Training Hub: Lightning-fast LoRA and QLoRA fine-tuning
Training Hub v0.4.0 adds LoRA and QLoRA fine-tuning powered by Unsloth, enabling fast, cost-effective model adaptation with roughly 70% less VRAM than full fine-tuning.
Scale LLM fine-tuning with Training Hub and OpenShift AI
A four-step pathway for scaling LLM fine-tuning from local experimentation to production deployment using Training Hub, OpenShift AI, Kubeflow Trainer, and AI pipelines.
Synthetic Data Generation for Smarter AI Workflows
IBM Technology explores how synthetic data generation with SDG Hub enables smarter AI workflows.
Synthetic data for RAG evaluation: Why your RAG system needs better testing
How SDG Hub enables teams to automatically create grounded evaluation datasets with question-answer-context triplets, transforming RAG tuning from intuition-driven to measurable.
How to simplify AI model fine-tuning
Red Hat explores how Training Hub simplifies AI model fine-tuning with a unified interface across multiple post-training algorithms.
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.
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.