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LICENSE.txt

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+BigScience RAIL License v1.0
+dated May 19, 2022
+
+
+
+
+This is a license (the “License”) between you (“You”) and the participants of BigScience (“Licensor”). Whereas the Apache 2.0 license was applicable to resources used to develop the Model, the licensing conditions have been modified for the access and distribution of the Model. This has been done to further BigScience’s aims of promoting not just open-access to its artifacts, but also a responsible use of these artifacts. Therefore, this Responsible AI License (RAIL)[1] aims at having an open and permissive character while striving for responsible use of the Model.
+
+
+                                                                    Section  I:  PREAMBLE
+
+
+BigScience is a collaborative open innovation project aimed at the responsible development and use of large multilingual datasets and Large Language Models (“LLM”), as well as, the documentation of best practices and tools stemming from this collaborative effort. Further, BigScience participants wish to promote collaboration and sharing of research artifacts - including the Model - for the benefit of society, pursuant to this License.
+
+
+The development and use of LLMs, and broadly artificial intelligence (“AI”), does not come without concerns. The world has witnessed how just a few companies/institutions are able to develop LLMs, and moreover, how Natural Language Processing techniques might, in some instances, become a risk for the public in general. Concerns might come in many forms, from racial discrimination to the treatment of sensitive information. 
+
+
+BigScience believes in the intersection between open and responsible AI development, thus, this License aims to strike a balance between both in order to enable responsible open-science for large language models and future NLP techniques. 
+This License governs the use of the BigScience BLOOM models (and their derivatives) and is informed by both the BigScience Ethical Charter and the model cards associated with the BigScience BLOOM models. BigScience has set forth its Ethical Charter representing the values of  its community. Although the BigScience community does not aim to impose its values on potential users of this Model, it is determined to take tangible steps towards protecting the community from inappropriate uses of the work being developed by BigScience.
+Furthermore, the model cards for the BigScience BLOOM models will inform the user about the limitations of the Model, and thus serves as the basis of some of the use-based restrictions in this License (See Part II).
+
+
+
+
+NOW THEREFORE, You and Licensor agree as follows:
+
+
+1. Definitions 
+
+
+1. "License" shall mean the terms and conditions for use, reproduction, and Distribution as defined in this document.
+2. “Data” means a collection of texts extracted from the BigScience Corpus used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License. The BigScience Corpus is a collection of existing sources of language data documented on the BigScience website.
+3. “Output” means the results of operating a Model as embodied in informational content resulting therefrom.
+4. “Model” means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the BigScience BLOOM model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part, on the Data using the Complementary Material. 
+5. “Derivatives of the Model” means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model.
+6. “Complementary Material” shall mean the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation. This includes any accompanying documentation, tutorials, examples etc.
+7. “Distribution” means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access. 
+8. “Licensor” means the copyright owner or entity authorized by the copyright owner that is granting the License, including the persons or entities that may have rights in the Model and/or distributing the Model.
+9. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator.
+10. “Third Parties” means individuals or legal entities that are not under common control with Licensor or You.
+11. "Contribution" shall mean any work of authorship, including the original version of the Model and any modifications or additions to that Model or Derivatives of the Model thereof, that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, “submitted” means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." 
+12. "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
+
+
+                                              Section II:   INTELLECTUAL PROPERTY RIGHTS
+Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
+2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
+3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is filed.
+
+
+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
+
+
+4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
+1. Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
+2. You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License; 
+3. You must cause any modified files to carry prominent notices stating that You changed the files; 
+4. You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model.
+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
+5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5). 
+6.  The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License. 
+
+
+Section IV: OTHER PROVISIONS
+7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License, update the Model through electronic means, or modify the Output of the Model based on updates. You shall undertake reasonable efforts to use the latest version of the Model.
+8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
+9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License.
+10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
+END OF TERMS AND CONDITIONS
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+Attachment A
+Use Restrictions
+You agree not to use the Model or Derivatives of the Model:
+1. In any way that violates any applicable national, federal, state, local or international law or regulation;
+2. For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+3. To generate or disseminate verifiably false information with the purpose of harming others;
+4. To generate or disseminate personal identifiable information that can be used to harm an individual;
+5. To generate or disseminate information or content, in any context (e.g. posts, articles, tweets, chatbots or other kinds of automated bots) without expressly and intelligibly disclaiming that the text is machine generated; 
+6. To defame, disparage or otherwise harass others;
+7. To impersonate or attempt to impersonate others;
+8. For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
+9. For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics
+10. To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+11. For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
+12. To provide medical advice and medical results interpretation; 
+13. To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigraton or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
+
+
+________________
+[1] https://arxiv.org/pdf/2011.03116.pdf

README.md

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+---
+license: bigscience-bloom-rail-1.0
+language:
+- ak
+- ar
+- as
+- bm
+- bn
+- ca
+- code
+- en
+- es
+- eu
+- fon
+- fr
+- gu
+- hi
+- id
+- ig
+- ki
+- kn
+- lg
+- ln
+- ml
+- mr
+- ne
+- nso
+- ny
+- or
+- pa
+- pt
+- rn
+- rw
+- sn
+- st
+- sw
+- ta
+- te
+- tn
+- ts
+- tum
+- tw
+- ur
+- vi
+- wo
+- xh
+- yo
+- zh
+- zhs
+- zht
+- zu
+pipeline_tag: text-generation
+---
+
+<h1 style='text-align: center '>BLOOM LM</h1> 
+<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2> 
+<h3 style='text-align: center '>Model Card</h3>
+<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+
+Version 1.0 / 26.May.2022
+
+## Table of Contents
+1. [Model Details](#model-details)
+2. [Uses](#uses)
+3. [Training Data](#training-data)
+4. [Risks and Limitations](#risks-and-limitations)
+5. [Evaluation](#evaluation)
+6. [Recommendations](#recommendations)
+7. [Glossary and Calculations](#glossary-and-calculations)
+8. [More Information](#more-information)
+9. [Model Card Authors](#model-card-authors)
+
+## Model Details  
+
+### Basics
+*This section provides information for anyone who wants to know about the model.*
+
+<details>
+<summary>Click to expand</summary> <br/>
+    
+**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
+
+* 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](#training-data)
+
+**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
+
+**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](https://huggingface.co)).
+
+* Organizations of contributors.  *(Further breakdown of organizations forthcoming.)*
+
+</details>
+
+### Technical Specifications
+*This section provides information for people who work on model development.*
+
+<details>
+<summary>Click to expand</summary><br/>
+
+Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
+
+**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
+
+* Decoder-only architecture
+
+* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
+
+* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
+
+* 7,069,016,064 parameters:
+
+    * 1,027,604,480 embedding parameters
+
+    * 30 layers, 32 attention heads
+
+    * Hidden layers are 4096-dimensional
+
+    * Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
+
+**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
+    
+**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
+
+* 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](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
+
+    *   DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
+
+    *   PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
+
+    *   apex ([Github link](https://github.com/NVIDIA/apex))
+
+
+#### **Training**
+
+ 
+Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)
+
+- 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)
+
+- Server training location: Île-de-France, France
+
+#### **Tokenization**
+    
+The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) 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.    
+    
+</details>
+
+
+### Environmental Impact
+
+<details>
+<summary>Click to expand</summary><br/>
+
+The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), 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.)*
+
+
+</details>
+<p>&nbsp;</p>
+
+## 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.*
+
+
+<details>
+<summary>Click to expand</summary><br/>
+    
+### 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://huggingface.co/spaces/bigscience/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](#high-stakes) settings is out of scope for this model.  The model is not designed for [critical decisions](#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](#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](#deception)
+
+-   Unconsented impersonation and imitation
+
+-   Unconsented surveillance 
+
+-   Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
+
+### 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](#intended-use)
+
+-   Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/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>
+<p>&nbsp;</p>
+
+## 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>
+<summary>Click to expand</summary><br/>
+    
+Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
+
+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](#tokenization) for more.)
+
+
+#### **Languages**
+    
+The pie chart shows the distribution of languages in training data.
+   
+![pie chart showing the distribution of languages in training data](https://github.com/bigscience-workshop/model_card/blob/main/assets/data/pie_chart.svg?raw=true)
+
+
+The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
+<details>
+<summary>Click to expand</summary><br/>
+    
+| Niger Congo    | Percentage |         | Indic     | Percentage |
+|----------------|------------ |------  |-----------|------------|
+| Chi Tumbuka    | 0.00002    |         | Assamese  | 0.01       |
+| Kikuyu         | 0.00004    |         | Odia      | 0.04       |
+| Bambara        | 0.00004    |         | Gujarati  | 0.04       |
+| Akan           | 0.00007    |         | Marathi   | 0.05       |
+| Xitsonga       | 0.00007    |         | Punjabi   | 0.05       |
+| Sesotho        | 0.00007    |         | Kannada   | 0.06       |
+| Chi Chewa      | 0.0001     |         | Nepali    | 0.07       |
+| Setswana       | 0.0002     |         | Telugu    | 0.09       |
+| Northern Sotho | 0.0002     |         | Malayalam | 0.10       |
+| Fon            | 0.0002     |         | Urdu      | 0.10       |
+| Kirundi        | 0.0003     |         | Tamil     | 0.20       |
+| Wolof          | 0.0004     |         | Bengali   | 0.50       |
+| Kuganda        | 0.0004     |         | Hindi     | 0.70       |
+| Chi Shona      | 0.001      |
+| Isi Zulu       | 0.001      |
+| Igbo           | 0.001      |
+| Xhosa          | 0.001      |
+| Kinyarwanda    | 0.003      |
+| Yoruba         | 0.006      |
+| Swahili        | 0.02       |
+</details>
+
+The following table shows the distribution of programming languages.
+<details>
+<summary>Click to expand</summary><br/>
+    
+| Extension      | Language   | Number of files |
+|----------------|------------|-----------------|
+| java           | Java       | 5,407,724       |
+| php            | PHP        | 4,942,186       |
+| cpp            | C++        | 2,503,930       |
+| py             | Python     | 2,435,072       |
+| js             | JavaScript | 1,905,518       |
+| cs             | C#         | 1,577,347       |
+| rb             | Ruby       | 6,78,413        |
+| cc             | C++        | 443,054         |
+| hpp            | C++        | 391,048         |
+| lua            | Lua        | 352,317         |
+| go             | GO         | 227,763         |
+| ts             | TypeScript | 195,254         |
+| C              | C          | 134,537         |
+| scala          | Scala      | 92,052          |
+| hh             | C++        | 67,161          |
+| H              | C++        | 55,899          |
+| tsx            | TypeScript | 33,107          |
+| rs             | Rust       | 29,693          |
+| phpt           | PHP        | 9,702           |
+| c++            | C++        | 1,342           |
+| h++            | C++        | 791             |
+| php3           | PHP        | 540             |
+| phps           | PHP        | 270             |
+| php5           | PHP        | 166             |
+| php4           | PHP        | 29              |
+    
+</details>    
+</details>
+<p>&nbsp;</p>
+
+## Risks and Limitations
+*This section identifies foreseeable harms and misunderstandings.*
+
+<details>
+<summary>Click to expand</summary><br/>
+    
+Model may:
+
+-   Overrepresent some viewpoints and underrepresent others
+
+-   Contain stereotypes
+  
+-   Contain [personal information](#personal-data-and-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
+</details>
+<p>&nbsp;</p>
+
+## Evaluation
+*This section describes the evaluation protocols and provides the results.*
+
+<details>
+<summary>Click to expand</summary><br/>
+
+### Metrics 
+*This section describes the different ways performance is calculated and why.*
+    
+Includes:
+
+| Metric             | Why chosen                                                         |
+|--------------------|--------------------------------------------------------------------|
+| [Perplexity](#perplexity)         | Standard metric for quantifying model improvements during training |
+| Cross Entropy [Loss](#loss) | Standard objective for language models.                            |
+
+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 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](#factors) and [Metrics](#metrics).*
+
+**Train-time Evaluation:**
+
+As of 25.May.2022, 15:00 PST:
+
+- Training Loss: 2.3
+
+- Validation Loss: 2.9
+
+- Perplexity: 16
+
+</details>
+<p>&nbsp;</p>
+
+## Recommendations
+
+*This section provides information on warnings and potential mitigations.*
+
+
+<details>
+<summary>Click to expand</summary><br/>
+
+-   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](#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.
+
+</details>
+<p>&nbsp;</p>
+
+## Glossary and Calculations
+
+*This section defines common terms and how metrics are calculated.*
+
+
+
+<details>
+<summary>Click to expand</summary><br/>
+
+-   <a name="loss">**Loss:**</a> 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. 
+
+-   <a name="perplexity">**Perplexity:**</a> 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. 
+
+-   <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
+
+-   <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
+
+-   <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
+
+-  <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
+  
+- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
+
+- <a name="deception">**Deception:**</a> 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.
+
+</details>
+<p>&nbsp;</p>
+
+## More Information
+
+<details>
+<summary>Click to expand</summary><br/>
+    
+### 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
+
+</details>
+<p>&nbsp;</p>
+    
+## Model Card Authors
+*Ordered roughly chronologically and by amount of time spent.*
+
+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
+

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+  "unk_token_id": 0,
+  "use_cache": true,
+  "vocab_size": 250880
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+}

special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3fa39cd4b1500feb205bcce3b9703a4373414cafe4970e0657b413f7ddd2a9d3
+size 14500438

tokenizer_config.json

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+{"unk_token": "<unk>", "eos_token": "</s>", "bos_token": "<s>", "pad_token": "<pad>", "name_or_path": "bigscience/tokenizer", "special_tokens_map_file": null, "tokenizer_class": "BloomTokenizerFast", "padding_side":"left"}