BLOOM (language model)
From Wikipedia, the free encyclopedia
| BLOOM | |
|---|---|
| Original author | BigScience research workshop |
| Initial release | July 12, 2022; 3 years ago (2022-07-12) |
| Written in | Python |
| License | BigScience Responsible AI License (RAIL) v1.0 |
| Website | bigscience |
| Repository | huggingface |
The BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) is an open-access large language model (LLM).[1] It was created by a volunteer-driven research effort to provide a transparently-created alternative to proprietary AI models.[2]
With 176 billion parameters, BLOOM is a transformer-based autoregressive model designed to generate text in 46 natural languages and 13 programming languages. The model, source code, and the data used to train it are all distributed under free licences, allowing for public research and use.[3][4]
BLOOM is the main outcome of the BigScience initiative, a one-year-long research workshop that took place from May 2021 to May 2022.[5] The project was led by HuggingFace and involved several hundred volunteer researchers and engineers from academia and the private sector. The model was trained between March and July 2022 on the Jean Zay public supercomputer in France, managed by GENCI and IDRIS (CNRS).[6]
BLOOM's training corpus, named ROOTS, combines data extracted from the then-latest version of the web-based OSCAR corpus (38% of ROOTS) and newly collected data extracted from a manually selected and documented list of language data sources. In total, the model was trained on approximately 366 billion (1.6TB) tokens.[7][8]
External links
References
- ↑ "BigScience Large Open-science Open-access Multilingual Language Model". Retrieved 2022-10-01.
- ↑ Heikkilä, Melissa (2022-07-12). "BLOOM: Inside the radical new project to democratize AI". MIT Technology Review. Retrieved 2023-12-26.
- ↑ "The BigScience RAIL license". Retrieved 2024-01-10.
- ↑ Le Scao T, Fan A, Akiki C, Pavlick E, Ilić S, Hesslow D, Castagné R, Luccioni A, Yvon F, Gallé M, Tow J, Rush AM, Biderman S, Webson A, Sasanka Ammanamanchi P, Wang T, Sagot B, Muennighoff N, Villanova del Moral A, Ruwase O, Bawden R, Bekman S, McMillan-Major A, Beltagy I, Nguyen H, Saulnier L, Tan S, Ortiz Suarez P, Sanh V, Laurençon H, Jernite Y, Launay J, Mitchell M, Raffel C, et al. (2022). "BLOOM: A 176B-Parameter Open-Access Multilingual Language Model". arXiv:2211.05100 [cs.CL].
- ↑ "BigScience". Retrieved 2024-01-10.
- ↑ "Release of largest trained open-science multilingual language model ever". French National Centre for Scientific Research. 2022-07-12. Retrieved 2023-12-26.
- ↑ Laurençon H, Saulnier L, Wang T, Akiki C, Villanova del Moral A, Le Scao T, Von Werra L, Mou C, González Ponferrada C, Nguyen H, Frohberg J, Šaško M, Lhoest Q, McMillan-Major A, Dupont G, Biderman S, Rogers A, Ben allal L, De Toni F, Pistilli G, Nguyen O, Nikpoor S, Masoud M, Colombo P, de la Rosa J, Villegas P, Thrush T, Longpre S, Nagel S, Weber L, Muñoz M, Zhu J, Van Strien D, Alyafeai Z, Almubarak K, Vu MC, Gonzalez-Dios I, Soroa A, Lo K, Dey M, Ortiz Suarez P, Gokaslan A, Bose S, Adelani D, Phan L, Tran H, Yu I, Pai S, Chim J, Lepercq V, Ilic S, Mitchell M, Luccioni S, Jernite Y (2022). "The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset". arXiv:2303.03915 [cs.CL].
- ↑ Heikkilä, Melissa (2022-07-12). "BLOOM: Inside the radical new project to democratize AI". MIT Technology Review. Retrieved 2023-12-26.
Large language models (LLMs) | |||||||
|---|---|---|---|---|---|---|---|
| Concepts |
| ||||||
| Training, prompting, and alignment | |||||||
| Models |
| ||||||
| Chatbots and assistants | |||||||
| Agents, coding, and applications | |||||||
| Software | |||||||
| Hardware and infrastructure | |||||||
| Benchmarks, evaluation, and detection | |||||||
| Datasets and data | |||||||
| Organizations | |||||||
| People | |||||||
| Social, economic, and governance | |||||||
 | This large language model-related article is a stub. You can help Wikipedia by adding missing information. |