BigScience and the BLOOM Model
BigScience was a year-long open research collaboration of over 1,000 researchers that produced BLOOM, one of the first truly multilingual, openly released large language models.
Overview
It matters as a landmark in transparent, community-driven AI built outside Big Tech.
Deep Dive
BigScience was a one-year research workshop running from 2021 to 2022, coordinated by Hugging Face and bringing together more than 1,000 volunteer researchers from over 60 countries and 250 institutions. Its headline output, released in July 2022, was BLOOM, a 176-billion-parameter autoregressive language model. BLOOM was deliberately multilingual, trained on the ROOTS corpus covering 46 natural languages and 13 programming languages, with strong representation of underrepresented languages such as several African and South Asian languages. Training ran for several months on the publicly funded Jean Zay supercomputer in France using around 384 GPUs. BLOOM was released under the Responsible AI License with full documentation of its data, training, and intended uses, contrasting sharply with the closed development of comparable models.
Technical Insight
BLOOM is a decoder-only transformer of similar scale to GPT-3, using ALiBi positional embeddings instead of learned position vectors, which helps it extrapolate to longer sequences than seen in training. It also applies an embedding layer normalization that improved training stability at scale. The multilingual ROOTS corpus was carefully assembled and documented so the language mix and data sources were transparent and auditable, a deliberate departure from opaque scraped datasets.
Strategic Impact
Vendor strategy
Vendor roadmaps influence what features your team can build next.
Cost and budget
Commercial terms and deployment options affect long-term cost and risk.
Risk and safety
Company incentives shape product defaults, safety posture, and openness.
The Future of BigScience and the BLOOM Model
BigScience demonstrated that large-scale, openly governed AI is possible, and its model influenced later open releases and the broader push for transparency. Future multilingual work will likely build on its lessons in data documentation and inclusive language coverage, while newer, more efficient models have surpassed BLOOM on raw capability. Its lasting legacy is the norm of publishing model cards, data statements, and responsible licenses, plus proof that public compute can train frontier-scale models.
Real-World Implementation
Generating and completing text across dozens of languages, including ones underserved by commercial models
Serving as an open research baseline for studying bias, multilingual transfer, and scaling behavior
Fine-tuning into task-specific or instruction-following variants such as BLOOMZ for non-English communities
Providing a fully documented model for academics studying training data provenance and responsible AI licensing
Risks & Guardrails
Launch announcements may outpace stability in real production workflows.
API pricing or policy shifts can break assumptions overnight.
Single-vendor dependency increases lock-in and migration costs.
Implementation Roadmap
Evaluate providers using your own tasks and datasets.
Review privacy, security, and legal terms before integration.
Maintain a fallback plan across models or vendors.
Monitor release notes so roadmap changes do not surprise teams.
Keep Exploring
Free newsletter
Keep up with AI in 3 minutes a day
One short email each weekday with the three AI stories that actually matter. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Take the BigScience and the BLOOM Model quiz
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
Next guide
Magic AI Long-Context Code Models
Frequently asked questions
What is BigScience and the BLOOM Model?
BigScience was a year-long open research collaboration of over 1,000 researchers that produced BLOOM, one of the first truly multilingual, openly released large language models. It matters as a landmark in transparent, community-driven AI built outside Big Tech.
What was the primary distinguishing goal of the BLOOM model compared to similar-sized models of its time?
BLOOM was designed to be genuinely multilingual and was released openly with full documentation, unlike closed models of comparable scale.
Approximately how many parameters does BLOOM have?
BLOOM is a 176-billion-parameter model, placing it on a similar scale to GPT-3.
How many natural languages was BLOOM trained to handle?
BLOOM's ROOTS training corpus covered 46 natural languages plus 13 programming languages.
Which organization coordinated the BigScience collaboration?
Hugging Face coordinated BigScience, a workshop of more than 1,000 volunteer researchers.
Where was BLOOM trained?
BLOOM was trained on the Jean Zay supercomputer in France, demonstrating that public compute could train a frontier-scale model.