What is BLOOM?
BLOOM is a 176-billion-parameter multilingual large language model produced by BigScience — a year-long open research collaboration of over a thousand researchers, coordinated by Hugging Face and trained on France's Jean Zay supercomputer. It was a landmark as the first openly developed LLM at that scale, designed for transparency and accessibility: trained in the open on a documented corpus spanning 46 natural languages and 13 programming languages, with weights released for anyone to study and use.
The architecture & training
BLOOM is a decoder-only transformer in the GPT lineage, trained on the multilingual ROOTS corpus assembled specifically for the project. Its deliberate emphasis on language diversity — including many languages underrepresented in earlier models, such as several African and South Asian languages — set it apart from English-centric LLMs. The family spans 560M, 1.1B, 1.7B, 3B, 7.1B and the flagship 176B, so you can pick a size to match your hardware.
What it is good at
BLOOM is strongest as a multilingual base model: text generation, completion and few-shot tasks across many languages, with notably better coverage of non-English languages than its contemporaries. It is widely used for research into large multilingual models, as a foundation to fine-tune (the instruction-tuned BLOOMZ variant followed), and for generation in languages other open models handled poorly. Smaller sizes make capable, runnable baselines. Its place in history is also part of the appeal: as the first openly built model at this scale, with its data and process documented, it remains a teaching reference for how a large language model is actually assembled.
Licensing & access
BLOOM uses the Responsible AI License (RAIL) — open and free to use, including commercially, but with use-based restrictions prohibiting harmful applications. The weights are on Hugging Face with full Transformers support. The 176B model is very large and needs serious multi-GPU hardware (or quantisation) to run, while the smaller sizes are practical on a single GPU.
Practical considerations
As an older, base model, BLOOM trails today's frontier LLMs on reasoning and instruction following — the 176B version is heavyweight to serve, and the pretrained checkpoints are not chat-aligned (use BLOOMZ or fine-tune for instructions). Read the RAIL licence terms, since they restrict certain uses, and prefer a smaller size unless you specifically need the full model's capacity.
How it compares
Versus GPT-Neo (EleutherAI's earlier open English-centric models) and T5 (Google's encoder-decoder), BLOOM's distinguishing strengths are scale and multilingual breadth. Newer open models like Falcon match or exceed it on English with more modern training, but BLOOM remains a reference point for open, multilingual LLM development and and a useful base model to build on whenever wide, genuine language coverage is what matters most.
Getting started
Load a BLOOM size that fits your GPU through Transformers and generate text in your target language; start with 560M–1.7B to prototype before scaling up. For instruction following, use the BLOOMZ variant or fine-tune a base checkpoint. To use the 176B model without local hardware, call it through a hosted inference provider instead, and always check the RAIL licence terms for your particular use case.
