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

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.

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

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

Out-of-Scope Use

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

Bias, Risks, and Limitations

This section identifies foreseeable harms and misunderstandings.

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

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.

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

Megatron-DeepSpeed (Github link)
DeepSpeed (Github link)
PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)
apex (Github link)