What is it?
Data quantity and quality play a vital role in determining the performance of Large Language Models (LLMs). High-quality data, in particular, can significantly boost the LLM’s ability to generalize on a wide range of downstream tasks. To cater to the requirements of the Granite models, we focused on a goal to produce a 10T dataset, named, GneissWeb, that is of higher quality than all other datasets of similar size available. Gneiss, pronounced as nice, is a strong and durable rock that is used for building and construction.
The GneissWeb recipe consists of sharded exact substring deduplication and a judiciously constructed ensemble of quality filters. We present the key evaluations that guided our design choices and provide filtering thresholds that can be used to filter the dataset to match the token and quality needs of Stage-1 (early pre-training) or Stage-2 (annealing) datasets.
Our evaluations demonstrate that GneissWeb outperforms state-of-the-art large open datasets (5T+ tokens). Specifically, ablation models trained on GneissWeb outperform those trained on FineWeb.V1.1 by 2.14 percentage points in terms of average score computed on a set of 11 benchmarks (both zero-shot and few-shot) commonly used to evaluate pre-train datasets. When the evaluation set is extended to 20 benchmarks (both zero-shot and few-shot), ablation models trained on GneissWeb outperform those trained on FineWeb.V1.1 by 1.49 percentage points. In future, we plan to release a detailed technical paper with fine grained details and the IBM Data Prep Kit to create the GneissWeb dataset.
Dataset Summary
Recently, IBM has introduced GneissWeb; a large dataset yielding around 10 trillion tokens that caters to the data quality and quantity requirements of training LLMs. The models trained using GneissWeb dataset outperform those trained on FineWeb 1.1.0 by 2.14 percentage points in terms of average score computed on a set of 11 commonly used benchmarks
Developers: IBM Research
Release Date: Feb 10th, 2025
License: Apache 2.0.
Usage