bloom-1b1

Version 1.0 / 26.May.2022

Table of Contents

1. Model Details
2. Uses
3. Training Data
4. Risks and Limitations
5. Evaluation
6. Recommendations
7. Glossary and Calculations
8. More Information
9. Model Card Authors

Model Details

Basics

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

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.

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

• 1,065,314,304 parameters:

  • 385,351,680 embedding parameters

  • 24 layers, 16 attention heads

  • Hidden layers are 1536-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:

  • Megatron-DeepSpeed (Github link)

  • DeepSpeed (Github link)

  • PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)

  • apex (Github link)

Training

Training logs: Tensorboard link

• Number of epochs: 1

• 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 and other model sizes)

• 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

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.

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](https://hugg