What is BLOOM?#

BigScience Large Open-science Open-access Multilingual Language Model

bloom

What is BLOOM?#

  • BLOOM is a 175-billion parameter model for language processing, able to generate text much like GPT-3 and OPT-175B.

  • It was developed to be multilingual, being deliberately trained on datasets containing 46 natural languages and 13 programming languages.

  • Unlike GPT-3, BLOOM is open-access, meaning anyone is able to download and use BLOOM for themselves.

  • Everything about BLOOM is openly available on the various pages within BigScience’s Hugging Face page, from the training logs to models of various sizes to checkpoints.

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.

Basics#

  • 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: 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).

Technical Specifications#

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 billion 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).

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 Data#

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.)

Languages#

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

pie chart showing the distribution of languages in training data

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

Preprocessing#

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.

Uses#

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#

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

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

Risks and Limitations#

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

Evaluation#

Metrics#

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 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

Glossary and Calculations#