bigscience / bloom-7b1-intermediate

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: [email protected]

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.)

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 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

• Megatron-DeepSpeed ( Github link )

• DeepSpeed ( Github link )

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

• apex ( Github link )

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 .

Training data includes:

• 45 natural languages

• 12 programming languages

• In 1.5TB 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.

The following tables shows the further distribution of Niger-Congo & Indic languages and programming languages in the training data.

Distribution of Niger Congo and Indic languages.

Distribution of programming languages.

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.

Estimated carbon emissions: (Forthcoming.)

Estimated electricity usage: (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

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 is not 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

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

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 what BLOOM models. Its focus is on those 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 .

Train-time Evaluation:

As of 25.May.2022, 15:00 PST:

• Training Loss: 2.0

• Validation Loss: 2.2

• Perplexity: 8.9

(More evaluation scores forthcoming.)

• 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.

• 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.

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