Model | Bloom

• Related Project: private
• Category: Paper Review
• Date: 2023-08-19

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

• huggingfcae-web: https://huggingface.co/docs/transformers/model_doc/bloom
• url: https://arxiv.org/abs/2211.05100
• pdf: https://arxiv.org/pdf/2211.05100
• demo: https://bloombot.ai/
• abstract: Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted fine-tuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Contents

• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
• TL;DR
  • Basics
  • Technical Specifications
    • Model Architecture and Objective
    • Compute infrastructure
      • Hardware
      • Software
  • Training
    • Training Data
  • Preprocessing
    • Speeds, Sizes, Times
    • Environmental Impact
  • Uses
    • How to use
    • Intended Use
    • Direct Use
    • Downstream Use
    • Misuse and Out-of-scope Use
      • Out-of-scope Uses
    • Misuse
  • Intended Users
    • Direct Users
    • Indirect Users
    • Others Affected (Parties Prenantes)
  • Risks and Limitations
  • Metrics
  • Factors
  • Results
  • Recommendations
  • Glossary and Calculations
  • More Information
    • Intermediate checkpoints
    • Dataset Creation
    • Technical Specifications
    • Lessons
    • Initial Results
    • Original checkpoints
      • Model Card Authors

TL;DR

1. 모델 개요: BLOOM은 1760억 개의 파라미터를 가진 자동회귀 대규모 언어모델로, 46개의 자연어와 13개의 프로그래밍 언어로 일관성 있는 텍스트를 생성할 수 있습니다.
2. 기술 사양: 디코더 전용 아키텍처, ALiBI 위치 인코딩, GeLU 활성화 함수를 사용하며 1.6TB의 텍스트 데이터셋으로 훈련되었습니다.

모델 개요

• 개발: BigScience
• 모델 타입: Transformer 기반 언어 모델
• 버전: 1.0.0
• 언어: 46개 자연어, 13개 프로그래밍 언어
• 라이선스: RAIL License v1.0

기술 사양

• 아키텍처: 디코더 전용, 70개 레이어, 112개 어텐션 헤드, 14336 차원 히든 레이어
• 파라미터 수: 1760억 개
• 위치 인코딩: ALiBI
• 활성화 함수: GeLU
• 목적함수: 교차 엔트로피(Cross Entropy) 평균 감소

training dataset 및 인프라

• 데이터 양: 1.6TB의 전처리된 텍스트, 3500억 개의 유니크 토큰
• 언어 분포: 영어 30%, 프랑스어 10%, 스페인어 10%, 기타 50%
• 프로그래밍 언어: Java, PHP, C++, Python, JavaScript 등
• 훈련 기간: 2022년 3월 11일 시작, 2022년 7월 5일 종료 예상
• 하드웨어: 384개의 A100 80GB GPU, 48개 노드

사용 사례

• 의도된 사용: 언어 생성, 정보 추출, 질문 응답, 요약 등의 작업
• 오용 방지: 생명공학, 정치, 법률, 금융 분야에서의 사용 금지, 개인 평가나 중요한 자동 결정에의 적용 금지

Basics

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. 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.

• Developed by: BigScience
• 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 (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.
  • Organizations of contributors. (Further breakdown of organizations forthcoming.)

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.

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

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
• 176,247,271,424 parameters:
  • 3,596,615,680 embedding 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

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

Training

This section provides information about the training data, the speed and size of training elements, and the environmental impact of training. It is useful for people who want to learn more about the model inputs and training footprint.

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.

Details for each dataset are provided in individual Data Cards, and the sizes of each of their contributions to the aggregated training data are presented in an Interactive Corpus Map.

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.)

Distribution of Niger Congo and Indic languages.

Distribution of programming languages.

Preprocessing

Tokenization: The BLOOM tokenizer, 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

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 is useful for anyone considering using the model or who is affected by the model.

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

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 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

• Users of derivatives created by Direct Users, such as those using software with an intended use
• Users of Derivatives of the Model, as described in the License

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

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

Metrics

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

Includes:

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

Results

Results are based on the Factors and Metrics.

Zero-shot evaluations:

WARNING: This section used to contain much more results, however they were not correct and we released without the approval of the evaluation working group. We are currently in the process of fixing the evaluations.

See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results

Train-time Evaluation:

Final checkpoint after 95K steps:

• Training Loss: 1.939
• Validation Loss: 2.061
• Perplexity: 7.045

For more see: https://huggingface.co/bigscience/tr11-176B-ml-logs

Recommendations

This section provides information on warnings and potential mitigations.

• 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 trained or finetuned downstream of BLOOM LM 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.

• Loss: A calculation of the difference between what the model has learned and what the data shows (“groundtruth”). The lower the loss, the better. The training process aims to minimize the loss.
• Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
• High-stakes settings: Such as those identified as “high-risk AI systems” and “unacceptable risk AI systems” in the European Union’s proposed Artificial Intelligence (AI) Act.
• Critical decisions: Such as those defined in the United States’ proposed Algorithmic Accountability Act.
• Human rights: Includes those rights defined in the Universal Declaration of Human Rights.
• Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as “personal data” in the European Union’s General Data Protection Regulation; and “personal information” in the Republic of South Africa’s Protection of Personal Information Act, The People’s Republic of China’s Personal information protection law.
• Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)
• Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.

More Information

This section provides links to writing on dataset creation, technical specifications, lessons learned, and initial results.

Intermediate checkpoints

For academic (or any) usage, we published the intermediate checkpoints, corresponding to the model state at each 5000 steps. Please follow this link to get these checkpoints.

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

Lessons

• 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

Original checkpoints

• The checkpoints in this repo correspond to the HuggingFace Transformers format. If you want to use our fork of Megatron-DeepSpeed that the model was trained with, you’d want to use this repo instead.
• Many intermediate checkpoints are available at https://huggingface.co/bigscience/bloom-intermediate/

Model Card Authors

Ordered roughly chronologically and by amount of time spent on creating this model card.

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