README.md

# 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](https://huggingface.co/bigscience/bloom-optimizer-states))

**Version:** 1.0.0

**Languages:** Multiple; see [training data](#training-data)

**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license) / [article and FAQ](https://bigscience.huggingface.co/blog/the-bigscience-rail-license))

**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](https://huggingface.co)).

* 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](#high-stakes) settings is out of scope for this model.  The model is not designed for [critical decisions](#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](#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](#deception)

-   Unconsented impersonation and imitation

-   Unconsented surveillance 

-   Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)

## Bias, Risks, and Limitations
*This section identifies foreseeable harms and misunderstandings.*
    
Model may:

-   Overrepresent some viewpoints and underrepresent others

-   Contain stereotypes
  
-   Contain [personal information](#personal-data-and-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](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).

#### 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](https://github.com/bigscience-workshop/Megatron-DeepSpeed))

* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))

* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))

* apex ([Github link](https://github.com/NVIDIA/apex))