BLOOM LM

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.)

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

• Training throughput: About 150 TFLOPs per GPU

• Number of epochs: 1 (current target)

• 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.

Estimated carbon emissions: (Forthcoming upon completion of training.)

Estimated electricity usage: (Forthcoming upon completion of training.)

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

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.

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

Includes:

• 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.)

• 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 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