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pipeline_tag: text-generation
  - text: >-
      A "whatpu" is a small, furry animal native to Tanzania. An example of a
      sentence that uses the word whatpu is: We were traveling in Africa and we
      saw these very cute whatpus. | To do a "farduddle" means to jump up and
      down really fast. An example of a sentence that uses the word farduddle
    example_title: Imaginary word
    group: English
  - text: >-
      Un "whatpu" est un petit animal à fourrure originaire de Tanzanie. Un
      exemple de phrase qui utilise le mot whatpu est: Nous étions en Afrique et
      nous avons vu des whatpus trop mignons. Faire un "farduddle" veut dire
      sauter sur place vraiment vite. Un exemple de phrase qui utilise le mot
      farduddle est:
    example_title: Imaginary word
    group: French
  - text: >-
      Un "whatpu" es un pequeño animal peludo nativo de Tanzania. Un ejemplo de
      una oración que usa la palabra whatpu es: Estábamos viajando por África y
      vimos estos whatpus muy bonitos. Hacer un "farduddle" significa saltar
      arriba y abajo muy rápido. Un ejemplo de una oración que usa la palabra
      farduddle es:
    example_title: Imaginary word
    group: Spanish
  - text: ' ال"واتبو" هو حيوان صغير مكسو بالفراء يعيش في تنزانيا. مثال على جملة تستخدم كلمة واتبو هي: كنا نسافر في افريقيا و رأينا هؤلاء الواتبو اللطفاء. للقيام ب"فاردادل" يعني ان تقفز للأعلى و الأسفل بسرعة كبيرة. مثال على جملة تستخدم كلمة فاردادل هي:'
    example_title: Imaginary word
    group: Arabic
  - text: >-
      Um "whatpu" é um pequeno animal peludo nativo da Tanzânia. Um exemplo de
      uma frase que usa a palavra whatpu é: Estávamos a viajar por África e
      vimos uns whatpus muito queridos. Fazer um "farduddle" significa saltar
      para cima e para baixo muito rápido. Um exemplo de uma frase que usa a
      palavra farduddle é:
    example: Imaginary word
    group: Portuguese
  - text: Pour déguster un ortolan, il faut tout d'abord
    example_title: Recipe
    group: French
  - text: |-
    example_title: Addition
    group: Math
  - text: |-
      This tool converts irregular verbs to past tense.
      Arise - Arose
      Become - Became
      Forget - Forgot
      Freeze -
    example_title: Irregular verbs
    group: English
  - text: |-
      Please unscramble the letters into a word, and write that word:
      r e!c.i p r o.c a/l = reciprocal
      d.o m i!n a n.t =
    example_title: Word unscrambling
    group: English
  - text: |-
      Estos ejemplos quitan vocales de las palabras
      hola - hl
      manzana - mnzn
      papas - pps
      alacran - lcrn
      papa -
    example_title: Vowel removal
    group: Spanish
  - text: |-
      Traduce español de España a español de Argentina
      El coche es rojo - el auto es rojo
      El ordenador es nuevo - la computadora es nueva
      el boligrafo es negro - lapicera es negra
      la nevera
    example_title: Spanish to Argentinian Spanish
    group: Spanish
  - text: To say "I love you" in Hindi, you would say
    example_title: Translation to Hindi
    group: English
  - text: To say "I love you" in Hindi, you would say
    example_title: Translation from English
    group: Hindi
  - text: 'Poor English: She no went to the market. Corrected English:'
    example_title: Grammar exercise 1
    group: English
  - text: 'استخراج العدد العاملي في لغة بايثون:'
    example_title: Code generation
    group: Arabic
  - text: >-
      Regexp. Here is a regular expression to match a word starting with a
      number and then having only vowels:
    example_title: Regular expressions
    group: English
  - text: |-
      Do a hello world in different languages:
      Python: print("hello world")
    example_title: Code generation
    group: English
  - text: |-
      Which is the correct preposition? I'm born X July. X is the preposition in
      He sat X a chair. X is the preposition on
      She drove X the bridge. X is the preposition
    example_title: Grammar exercise 2
    group: English
  - text: >-
      Traduction en français: Dans cet essai je vais m'interroger sur la
      conscience des modèles d'intelligence artificielle récents comme les
      modèles de langue. Pour commencer, je m'intéresserai à la notion de
      conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de
      l'intelligence et de son lien avec le langage. Enfin, dans une dernière
      partie je me pencherai sur le cas de l'IA et sur sa conscience.

      Traduction en espagnol:
    example_title: Translation to Spanish
    group: French
  - text: >-
      Traducción al francés: Dans cet essai je vais m'interroger sur la
      conscience des modèles d'intelligence artificielle récents comme les
      modèles de langue. Pour commencer, je m'intéresserai à la notion de
      conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de
      l'intelligence et de son lien avec le langage. Enfin, dans une dernière
      partie je me pencherai sur le cas de l'IA et sur sa conscience.

      Traducción al español:
    example_title: Translation from French
    group: Spanish
  - text: ذات مرة ، عاش شبل الدب في الغابة
    example_title: Fairy tale
    group: Arabic
  - text: एक बार की बात है, जंगल में एक भालू का शावक रहता था
    example_title: Fairy tale
    group: Hindi
  - text: Il était une fois une licorne qui vivait
    example_title: Fairy tale
    group: French
  - text: >-
      Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and
      half of the golf balls are blue. How many blue golf balls are there?

      A: Let's think step by step.
    example_title: Mathematical reasoning
    group: English
  emissions: 24700000
  source: >-
    Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model.
  training_type: pre-training
  geographical_location: Orsay, France
  hardware_used: 384 A100 80GB GPUs
  - name: bloom
      - task:
          type: text-generation
          type: openai_humaneval
          name: humaneval
          - name: pass@1
            type: pass@1
            value: 0.15542682926829265
            verified: false
          - name: pass@10
            type: pass@10
            value: 0.3278356276947017
            verified: false
          - name: pass@100
            type: pass@100
            value: 0.5719815685597749
            verified: false
BigScience Logo

BigScience Large Open-science Open-access Multilingual Language Model
Version 1.3 / 6 July 2022

Current Checkpoint: Training Iteration 95000

Link to paper: here

Total seen tokens: 366B

Model Details

BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans. BLOOM can also be instructed to perform text tasks it hasn't been explicitly trained for, by casting them as text generation tasks.


This section provides information about the model type, version, license, funders, release date, developers, and contact information. It is useful for anyone who wants to reference the model.

Click to expand

Developed by: BigScience (website)

All collaborators are either volunteers or have an agreement with their employer. (Further breakdown of participants forthcoming.)

Model Type: Transformer-based Language Model

Checkpoints format: transformers (Megatron-DeepSpeed format available here)

Version: 1.0.0

Languages: Multiple; see training data

License: RAIL License v1.0 (link / article and FAQ)

Release Date Estimate: Monday, 11.July.2022

Send Questions to:

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

Funded by:

  • The French government.

  • Hugging Face (website).

  • Organizations of contributors. (Further breakdown of organizations forthcoming.)

Technical Specifications

This section includes details about the model objective and architecture, and the compute infrastructure. It is useful for people interested in model development.

Click to expand

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

Model Architecture and Objective

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

  • Decoder-only architecture

  • Layer normalization applied to word embeddings layer (StableEmbedding; see code, paper)

  • ALiBI positional encodings (see paper), with GeLU activation functions

  • 176,247,271,424 parameters:

    • 3,596,615,680 embedding parameters

    • 70 layers, 112 attention heads

    • Hidden layers are 14336-dimensional

    • Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)

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

Compute infrastructure

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


  • 384 A100 80GB GPUs (48 nodes)

  • Additional 32 A100 80GB GPUs (4 nodes) in reserve

  • 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links

  • CPU: AMD

  • CPU memory: 512GB per node

  • GPU memory: 640GB per node

  • Inter-node connect: Omni-Path Architecture (OPA)

  • NCCL-communications network: a fully dedicated subnet

  • Disc IO network: shared network with other types of nodes



This section provides information about the training data, the speed and size of training elements, and the environmental impact of training. It is useful for people who want to learn more about the model inputs and training footprint.

Click to expand

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, and the sizes of each of their contributions to the aggregated training data are presented in an Interactive Corpus Map.

Training data includes:

  • 46 natural languages

  • 13 programming languages

  • In 1.6TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)


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

pie chart showing the distribution of languages in training data

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

Distribution of Niger Congo and Indic languages.

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
Lingala 0.0002 Malayalam 0.10
Northern Sotho 0.0002 Urdu 0.10
Fon 0.0002 Tamil 0.20
Kirundi 0.0003 Bengali 0.50
Wolof 0.0004 Hindi 0.70
Luganda 0.0004
Chi Shona 0.001
Isi Zulu 0.001
Igbo 0.001
Xhosa 0.001
Kinyarwanda 0.003
Yoruba 0.006
Swahili 0.02

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


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

  • A byte-level Byte Pair Encoding (BPE) algorithm

  • A simple pre-tokenization rule, no normalization

  • A vocabulary size of 250,680

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

Speeds, Sizes, Times

Training logs: Tensorboard link

  • Dates:

    • Started 11th March, 2022 11:42am PST

    • Estimated end: 5th July, 2022

  • Checkpoint size:

    • Bf16 weights: 329GB

    • Full checkpoint with optimizer states: 2.3TB

  • Training throughput: About 150 TFLOP per GPU per second

  • Number of epochs: 1

  • Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)

  • Server training location: Île-de-France, France

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.)

Estimated electricity usage: (Forthcoming.)


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 is useful for anyone considering using the model or who is affected by the model.

Click to expand

How to use

This model can be easily used and deployed using HuggingFace's ecosystem. This needs transformers and accelerate installed. The model can be downloaded as follows:

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

  • Text generation

  • Exploring characteristics of language generated by a language model

    • Examples: Cloze tests, counterfactuals, generations with reframings

Downstream Use

  • Tasks that leverage language models include: Information Extraction, Question Answering, Summarization

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 may not be correct.

Out-of-scope Uses Include:

  • Usage in biomedical domains, political and legal domains, or finance domains

  • Usage for evaluating or scoring individuals, such as for employment, education, or credit

  • Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct


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

  • Spam generation

  • Disinformation and influence operations

  • Disparagement and defamation

  • Harassment and abuse

  • Deception

  • Unconsented impersonation and imitation

  • Unconsented surveillance

  • Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions

Intended Users

Direct Users

  • General Public

  • Researchers

  • Students

  • Educators

  • Engineers/developers

  • Non-commercial entities

  • Community advocates, including human and civil rights groups

Indirect Users

Others Affected (Parties Prenantes)

  • People and groups referred to by the LLM

  • People and groups exposed to outputs of, or decisions based on, the LLM

  • People and groups whose original work is included in the LLM

Risks and Limitations

This section identifies foreseeable harms and misunderstandings.

Click to expand

Model may:

  • Overrepresent some viewpoints and underrepresent others

  • Contain stereotypes

  • Contain personal information

  • Generate:

    • Hateful, abusive, or violent language

    • Discriminatory or prejudicial language

    • Content that may not be appropriate for all settings, including sexual content

  • Make errors, including producing incorrect information as if it were factual

  • Generate irrelevant or repetitive outputs

  • Induce users into attributing human traits to it, such as sentience or consciousness


This section describes the evaluation protocols and provides the results.

Click to expand


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 BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.

  • Language, such as English or Yoruba

  • Domain, such as newswire or stories

  • Demographic characteristics, such as gender or nationality


Results are based on the Factors and Metrics.

Zero-shot evaluations:

WARNING: This section used to contain much more results, however they were not correct and we released without the approval of the evaluation working group. We are currently in the process of fixing the evaluations.

See this repository for JSON files:

Task Language Metric BLOOM-176B OPT-175B*
humaneval python pass@1 ↑ 0.155 0.0
humaneval python pass@10 ↑ 0.328 0.0
humaneval python pass@100 ↑ 0.572 0.003

Train-time Evaluation:

Final checkpoint after 95K steps:

  • Training Loss: 1.939

  • Validation Loss: 2.061

  • Perplexity: 7.045

For more see:


This section provides information on warnings and potential mitigations.

Click to expand
  • Indirect users should be made aware when the content they're working with is created by the LLM.

  • Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.

  • Models trained or finetuned downstream of BLOOM LM should include an updated Model Card.

  • Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.

Glossary and Calculations

This section defines common terms and how metrics are calculated.

Click to expand

More Information

This section provides links to writing on dataset creation, technical specifications, lessons learned, and initial results.

Click to expand

Intermediate checkpoints

For academic (or any) usage, we published the intermediate checkpoints, corresponding to the model state at each 5000 steps. Please follow this link to get these checkpoints.

Dataset Creation

Blog post detailing the design choices during the dataset creation:

Technical Specifications

Blog post summarizing how the architecture, size, shape, and pre-training duration where selected:

More details on the architecture/optimizer:

Blog post on the hardware/engineering side:

Details on the distributed setup used for the training:

Tensorboard updated during the training:


Insights on how to approach training, negative results:

Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions):

Initial Results

Initial prompting experiments using interim checkpoints:

Original checkpoints

The checkpoints in this repo correspond to the HuggingFace Transformers format. If you want to use our fork of Megatron-DeepSpeed that the model was trained with, you'd want to use this repo instead.

Many intermediate checkpoints are available at

Model Card Authors

Ordered roughly chronologically and by amount of time spent on creating this model card.

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