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BLOOM LM

BigScience Large Open-science Open-access Multilingual Language Model

Model Card

Version 1.0 / 26.May.2022

Table of Contents

  • Model Details
  • Uses
  • Training Data
  • Risks and Limitations
  • Evaluation
  • Recommendations
  • Glossary and Calculations
  • More Information
  • Model Card Authors
  • Model Details

    Basics

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

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    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

    Version: 1.0.0

    Languages: Multiple; see training data

    License: RAIL License v1.0 ( link )

    Release Date Estimate: Monday, 11.July.2022

    Send Questions to: bigscience-contact@googlegroups.com

    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 provides information for people who work on model development.

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

    Model Architecture: 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

    • 7,069,016,064 parameters:

      • 1,027,604,480 embedding parameters

      • 30 layers, 32 attention heads

      • Hidden layers are 4096-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 ).

    • Hardware: 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

    • Software:

    Training

    Training logs: Tensorboard link

    • Number of epochs: 1 ( current target )

    • Dates:

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

      • Ended 5th July, 2022

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

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

    Tokenization

    The BLOOM tokenizer ( link ) is 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.

    Environmental Impact

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    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.)

    Uses

    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.

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    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 is not 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

    Misuse

    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

    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.

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    Details for each dataset are provided in individual Data Cards .

    Training data includes:

    • 45 natural languages

    • 12 programming languages

    • In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)

    Languages

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

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

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    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.

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    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

    Risks and Limitations

    This section identifies foreseeable harms and misunderstandings.

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    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

    Evaluation

    This section describes the evaluation protocols and provides the results.

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    Metrics

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

    Includes:

    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.)

    Factors

    This section lists some different aspects of BLOOM models. Its focus is on those 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

    Results are based on the Factors and Metrics .

    Train-time Evaluation:

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

    • Training Loss: 2.3

    • Validation Loss: 2.9

    • Perplexity: 16

    Recommendations

    This section provides information on warnings and potential mitigations.

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    • 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 pretrained with the LLM 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.

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    More Information

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    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