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--- |
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datasets: |
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- bigscience/xP3 |
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license: bigscience-bloom-rail-1.0 |
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language: |
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- ak |
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- ar |
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- as |
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- bm |
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- bn |
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- ca |
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- code |
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- en |
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- es |
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- eu |
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- fon |
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- fr |
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- gu |
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- hi |
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- id |
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- ig |
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- ki |
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- kn |
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- lg |
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- ln |
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- ml |
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- mr |
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- ne |
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- nso |
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- ny |
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- or |
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- pa |
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- pt |
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- rn |
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- rw |
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- sn |
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- st |
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- sw |
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- ta |
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- te |
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- tn |
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- ts |
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- tum |
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- tw |
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- ur |
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- vi |
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- wo |
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- xh |
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- yo |
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- zh |
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- zu |
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programming_language: |
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- C |
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- C++ |
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- C# |
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- Go |
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- Java |
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- JavaScript |
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- Lua |
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- PHP |
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- Python |
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- Ruby |
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- Rust |
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- Scala |
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- TypeScript |
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pipeline_tag: text-generation |
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widget: |
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- text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous review as positive, neutral or negative?" |
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example_title: "zh-en sentiment" |
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- text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？" |
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example_title: "zh-zh sentiment" |
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- text: "Suggest at least five related search terms to \"Mạng neural nhân tạo\"." |
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example_title: "vi-en query" |
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- text: "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels»." |
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example_title: "fr-fr query" |
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- text: "Explain in a sentence in Telugu what is backpropagation in neural networks." |
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example_title: "te-en qa" |
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- text: "Why is the sky blue?" |
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example_title: "en-en qa" |
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- text: "Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is \"Heroes Come in All Shapes and Sizes\". Story (in Spanish):" |
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example_title: "es-en fable" |
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- text: "Write a fable about wood elves living in a forest that is suddenly invaded by ogres. The fable is a masterpiece that has achieved praise worldwide and its moral is \"Violence is the last refuge of the incompetent\". Fable (in Hindi):" |
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example_title: "hi-en fable" |
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--- |
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# Table of Contents |
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1. [Model Summary](#model=summary) |
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2. [Use](#use) |
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3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) |
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4. [Training Details](#training-details) |
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5. [Evaluation](#evaluation) |
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6. [Environmental Impact](#environmental-impact) |
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7. [Citation](#citation) |
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8. [Model Card Authors](#model-card-authors) |
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9. [How To Get Started With the Model](#how-to-get-started-with-the-model) |
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# Model Summary |
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> We present BLOOMZ & mT0, a family of models capable of following human instructions in hundreds of languages. By finetuning large BLOOM & mT5 pretrained multilingual language models on our multilingual task mixture (xP3), we discover various generalization properties of our finetuned models acrosss tasks and languages. |
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- **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf) |
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- **Paper:** [TODO] |
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- **Funded by:** The French government & Hugging Face |
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- **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co) |
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- **BLOOMZ & mT0 Model Family:** |
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|Name|Explanation| |
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|----|-----------| |
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|[bloomz-560m](https://huggingface.co/bigscience/bloomz-560m)| 560M parameter multitask finetuned version of [bloom-560m](https://huggingface.co/bigscience/bloom-560m) on [xP3](https://huggingface.co/bigscience/xP3)| |
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|[bloomz-1b1](https://huggingface.co/bigscience/bloomz-1b1)| 1.1B parameter multitask finetuned version of [bloom-1b1](https://huggingface.co/bigscience/bloom-1b1) on [xP3](https://huggingface.co/bigscience/xP3)| |
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|[bloomz-1b7](https://huggingface.co/bigscience/bloomz-1b7)| 1.7B parameter multitask finetuned version of [bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on [xP3](https://huggingface.co/bigscience/xP3)| |
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|[bloomz-3b](https://huggingface.co/bigscience/bloomz-3b)| 3B parameter multitask finetuned version of [bloom-3b](https://huggingface.co/bigscience/bloom-3b) on [xP3](https://huggingface.co/bigscience/xP3)| |
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|[bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1)|7.1B parameter multitask finetuned version of [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) on [xP3](https://huggingface.co/bigscience/xP3)| |
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|[bloomz](https://huggingface.co/bigscience/bloomz)|176B parameter multitask finetuned version of [bloom](https://huggingface.co/bigscience/bloom) on [xP3](https://huggingface.co/bigscience/xP3)| |
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|[bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt)|7.1B parameter multitask finetuned version of [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) on [xP3](https://huggingface.co/bigscience/xP3) & [xP3mt](https://huggingface.co/bigscience/xP3mt). **Better than [bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1) when prompting in non-English**| |
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|[bloomz-mt](https://huggingface.co/bigscience/bloomz-mt)| 176B parameter multitask finetuned version of [bloom](https://huggingface.co/bigscience/bloom) on [xP3](https://huggingface.co/bigscience/xP3) & [xP3mt](https://huggingface.co/bigscience/xP3mt). **Better than [bloomz](https://huggingface.co/bigscience/bloomz) when prompting in non-English**| |
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|[bloomz-7b1-p3](https://huggingface.co/bigscience/bloomz-7b1)| 7.1B parameter multitask finetuned version of [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) on [P3](https://huggingface.co/bigscience/P3). **Released for research purposes, performance is inferior to [bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1)**| |
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|[bloomz-p3](https://huggingface.co/bigscience/bloomz)| 176B parameter multitask finetuned version of [bloom](https://huggingface.co/bigscience/bloom) on [P3](https://huggingface.co/bigscience/P3). **Released for research purposes, performance is inferior to [bloomz](https://huggingface.co/bigscience/bloomz)**| |
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|[mt0-small](https://huggingface.co/bigscience/mt0-xxl)|300M parameter multitask finetuned version of [mt5-small](https://huggingface.co/google/mt5-small) on [xP3](https://huggingface.co/bigscience/xP3)| |
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|[mt0-base](https://huggingface.co/bigscience/mt0-xxl)|580M parameter multitask finetuned version of [mt5-base](https://huggingface.co/google/mt5-base) on [xP3](https://huggingface.co/bigscience/xP3)| |
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|[mt0-large](https://huggingface.co/bigscience/mt0-xxl)|1.2B parameter multitask finetuned version of [mt5-large](https://huggingface.co/google/mt5-large) on [xP3](https://huggingface.co/bigscience/xP3)| |
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|[mt0-xl](https://huggingface.co/bigscience/mt0-xxl)|3.7B parameter multitask finetuned version of [mt5-xl](https://huggingface.co/google/mt5-xl) on [xP3](https://huggingface.co/bigscience/xP3)| |
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|[mt0-xxl](https://huggingface.co/bigscience/mt0-xxl)|13B parameter multitask finetuned version of [mt5-xxl](https://huggingface.co/google/mt5-xxl) on [xP3](https://huggingface.co/bigscience/xP3)| |
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|[mt0-xxl-mt](https://huggingface.co/bigscience/mt0-xxl-mt)|13B parameter multitask finetuned version of [mt5-xxl](https://huggingface.co/google/mt5-xxl) on [xP3](https://huggingface.co/bigscience/xP3) & [xP3mt](https://huggingface.co/bigscience/xP3mt). **Better than [mt0-xxl](https://huggingface.co/bigscience/mt0-xxl) when prompting in non-English**| |
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|[mt0-xxl-p3](https://huggingface.co/bigscience/mt0-xxl-p3)| 13B parameter multitask finetuned version of [mt5-xxl](https://huggingface.co/google/mt5-xxl) on [P3](https://huggingface.co/bigscience/P3). **Released for research purposes, performance is inferior to [mt0-xxl](https://huggingface.co/bigscience/mt0-xxl)**| |
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|----|-----------| |
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# Intended uses |
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You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. For instance, you can ask *"Translate this to Chinese: Je t'aime."*, and the model will hopefully generate *"我爱你"*. |
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# How to use |
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Here is how to use the model in PyTorch: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m") |
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model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m") |
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inputs = tokenizer.encode("Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy", return_tensors="pt") |
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outputs = model.generate(inputs) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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To use another checkpoint, replace the path in `AutoTokenizer` and `AutoModelForCausalLM`. |
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**Note: 176B models are trained with bfloat16, while smaller models are trained with fp16. We recommend using the same precision type or fp32 at inference** |
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# Limitations |
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- Large model size may require large computational resources |
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- High performance variance depending on the prompt |
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# BibTeX entry and citation info |
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```bibtex |
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TODO |
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``` |
