Bloom 1b7

🚀 BLOOM LM

BigScience Large Open-science Open-access Multilingual Language Model. This model enables public research on large language models, supporting multiple languages and text generation.

📚 Table of Contents

1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Recommendations
5. Training Data
6. Evaluation
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary and Calculations
11. More Information
12. Model Card Authors
13. Model Card Contact

✨ Model Details

Model Description

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
• Funded by:
  • The French government.
  • Hugging Face (website).
  • Organizations of contributors. (Further breakdown of organizations forthcoming.)

🛠️ Uses

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

⚠️ 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

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 pretrained with the LLM 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.

📊 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 table shows the further distribution of Niger-Congo and Indic languages in the training data.

The following table shows the distribution of programming languages.

🧪 Evaluation

This section describes the evaluation protocols and provides the results.

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 at the end of model training.)

• BLOOM Book: Read generations from BLOOM based on prompts provided by the community

🌱 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.)

🔧 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
• 1,722,408,960 parameters:
  • 513,802,240 embedding parame