<h1 style='text-align: center '>BLOOM LM</h1> <h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2> <h3 style='text-align: center '>Model Card</h3> <img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

Version 1.0 / 26.May.2022

Model Card for Bloom-560m

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Table of Contents

  1. Model Details
  2. Uses
  3. Bias, Risks, and Limitations
  4. Recommendations
  5. Training Data
  6. Evaluation
  7. Environmental Impact
  8. Technical Specifications
  9. Citation
  10. Glossary and Calculations
  11. More Information
  12. Model Card Authors
  13. Model Card Contact

Model Details

Model Description

This section provides information for anyone who wants to know about the model.


This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. It provides information for anyone considering using the model or who is affected by the model.

Intended Use

This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.

Direct Use

Downstream Use

Misuse and Out-of-scope Use

This section addresses what users ought not do with the model.

See the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.

Out-of-scope Uses

Using the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.

Out-of-scope Uses Include:


Intentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:

Intended Users

Direct Users

Indirect Users

Others Affected (Parties Prenantes)

Bias, Risks and Limitations

This section identifies foreseeable harms and misunderstandings.

Model may:


This section provides information on warnings and potential mitigations.

Training Data

This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.

Details for each dataset are provided in individual Data Cards.

Training data includes:


The pie chart shows the distribution of languages in training data.

pie chart showing the distribution of languages in training data

The following table shows the further distribution of Niger-Congo and Indic languages in the training data.

Niger Congo Percentage Indic Percentage
Chi Tumbuka 0.00002 Assamese 0.01
Kikuyu 0.00004 Odia 0.04
Bambara 0.00004 Gujarati 0.04
Akan 0.00007 Marathi 0.05
Xitsonga 0.00007 Punjabi 0.05
Sesotho 0.00007 Kannada 0.06
Chi Chewa 0.0001 Nepali 0.07
Setswana 0.0002 Telugu 0.09
Northern Sotho 0.0002 Malayalam 0.10
Fon 0.0002 Urdu 0.10
Kirundi 0.0003 Tamil 0.20
Wolof 0.0004 Bengali 0.50
Kuganda 0.0004 Hindi 0.70
Chi Shona 0.001
Isi Zulu 0.001
Igbo 0.001
Xhosa 0.001
Kinyarwanda 0.003
Yoruba 0.006
Swahili 0.02

The following table shows the distribution of programming languages.

Extension Language Number of files
java Java 5,407,724
php PHP 4,942,186
cpp C++ 2,503,930
py Python 2,435,072
js JavaScript 1,905,518
cs C# 1,577,347
rb Ruby 6,78,413
cc C++ 443,054
hpp C++ 391,048
lua Lua 352,317
go GO 227,763
ts TypeScript 195,254
C C 134,537
scala Scala 92,052
hh C++ 67,161
H C++ 55,899
tsx TypeScript 33,107
rs Rust 29,693
phpt PHP 9,702
c++ C++ 1,342
h++ C++ 791
php3 PHP 540
phps PHP 270
php5 PHP 166
php4 PHP 29


This section describes the evaluation protocols and provides the results.


This section describes the different ways performance is calculated and why.


Metric Why chosen
Perplexity Standard metric for quantifying model improvements during training
Cross Entropy Loss Standard objective for language models.

And multiple different metrics for specific tasks. (More evaluation metrics forthcoming upon completion of evaluation protocol.)


This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.


Results are based on the Factors and Metrics.

Train-time Evaluation:

As of 25.May.2022, 15:00 PST:

(More evaluation scores forthcoming at the end of model training.)

Environmental Impact

The training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.

Estimated carbon emissions: (Forthcoming upon completion of training.)

Estimated electricity usage: (Forthcoming upon completion of training.)

Technical Specifications

This section provides information for people who work on model development.

Please see the BLOOM training README for full details on replicating training.

Model Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):

Objective Function: Cross Entropy with mean reduction (see API documentation).

Compute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).


Training logs: Tensorboard link


The BLOOM tokenizer (link) is a learned subword tokenizer trained using:

It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.


Cite as: BigScience, BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model. International, May 2021-May 2022

Glossary and Calculations

This section defines common terms and how metrics are calculated.

More Information

Dataset Creation

Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling

Technical Specifications

Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours

More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml

Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model

Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml

Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss

Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md

Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md

Initial Results

Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book

Model Card Authors

Ordered roughly chronologically and by amount of time spent.

Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff

Model Card Contact

Send Questions to: bigscience-contact@googlegroups.com