In this session we will go over how we created GneissWeb and discuss tools and techniques used. We will provide code examples that you can try at your leisure.

👉 > 2% avg improvement in benchmark performance over FineWeb 👉 Huggingface page 👉 Data prep kit detailed recipe 👉 Data prep kit bloom filter for quick reproduction 👉 Recipe models for reproduction 👉 announcement 👉 Paper

At IBM, responsible AI implies transparency in training data: Introducing GneissWeb (pronounced “niceWeb”), a state-of-the-art LLM pre-training dataset with ~10 Trillion tokens derived from FineWeb, with open recipes, results, and tools for reproduction! In this session we will go over how we created GneissWeb and discuss tools and techniques used. We will provide code examples that you can try at your leisure.