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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.17557 |
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| _version_ | 1866929569968160768 |
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| author | Penedo, Guilherme Kydlíček, Hynek allal, Loubna Ben Lozhkov, Anton Mitchell, Margaret Raffel, Colin Von Werra, Leandro Wolf, Thomas |
| author_facet | Penedo, Guilherme Kydlíček, Hynek allal, Loubna Ben Lozhkov, Anton Mitchell, Margaret Raffel, Colin Von Werra, Leandro Wolf, Thomas |
| contents | The performance of a large language model (LLM) depends heavily on the quality and size of its pretraining dataset. However, the pretraining datasets for state-of-the-art open LLMs like Llama 3 and Mixtral are not publicly available and very little is known about how they were created. In this work, we introduce FineWeb, a 15-trillion token dataset derived from 96 Common Crawl snapshots that produces better-performing LLMs than other open pretraining datasets. To advance the understanding of how best to curate high-quality pretraining datasets, we carefully document and ablate all of the design choices used in FineWeb, including in-depth investigations of deduplication and filtering strategies. In addition, we introduce FineWeb-Edu, a 1.3-trillion token collection of educational text filtered from FineWeb. LLMs pretrained on FineWeb-Edu exhibit dramatically better performance on knowledge- and reasoning-intensive benchmarks like MMLU and ARC. Along with our datasets, we publicly release our data curation codebase and all of the models trained during our ablation experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale Penedo, Guilherme Kydlíček, Hynek allal, Loubna Ben Lozhkov, Anton Mitchell, Margaret Raffel, Colin Von Werra, Leandro Wolf, Thomas Computation and Language The performance of a large language model (LLM) depends heavily on the quality and size of its pretraining dataset. However, the pretraining datasets for state-of-the-art open LLMs like Llama 3 and Mixtral are not publicly available and very little is known about how they were created. In this work, we introduce FineWeb, a 15-trillion token dataset derived from 96 Common Crawl snapshots that produces better-performing LLMs than other open pretraining datasets. To advance the understanding of how best to curate high-quality pretraining datasets, we carefully document and ablate all of the design choices used in FineWeb, including in-depth investigations of deduplication and filtering strategies. In addition, we introduce FineWeb-Edu, a 1.3-trillion token collection of educational text filtered from FineWeb. LLMs pretrained on FineWeb-Edu exhibit dramatically better performance on knowledge- and reasoning-intensive benchmarks like MMLU and ARC. Along with our datasets, we publicly release our data curation codebase and all of the models trained during our ablation experiments. |
| title | The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.17557 |