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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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model-index: |
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- name: bloomz-560m |
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results: |
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- task: |
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type: Coreference resolution |
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dataset: |
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type: winogrande |
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name: Winogrande XL (xl) |
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config: xl |
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split: validation |
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revision: a80f460359d1e9a67c006011c94de42a8759430c |
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metrics: |
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- type: Accuracy |
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value: 52.41 |
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- task: |
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type: Coreference resolution |
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dataset: |
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type: Muennighoff/xwinograd |
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name: XWinograd (en) |
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config: en |
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split: test |
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revision: 9dd5ea5505fad86b7bedad667955577815300cee |
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metrics: |
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- type: Accuracy |
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value: 51.01 |
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- task: |
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type: Coreference resolution |
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dataset: |
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type: Muennighoff/xwinograd |
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name: XWinograd (fr) |
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config: fr |
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split: test |
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revision: 9dd5ea5505fad86b7bedad667955577815300cee |
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metrics: |
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- type: Accuracy |
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value: 51.81 |
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- task: |
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type: Coreference resolution |
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dataset: |
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type: Muennighoff/xwinograd |
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name: XWinograd (jp) |
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config: jp |
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split: test |
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revision: 9dd5ea5505fad86b7bedad667955577815300cee |
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metrics: |
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- type: Accuracy |
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value: 52.03 |
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- task: |
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type: Coreference resolution |
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dataset: |
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type: Muennighoff/xwinograd |
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name: XWinograd (pt) |
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config: pt |
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split: test |
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revision: 9dd5ea5505fad86b7bedad667955577815300cee |
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metrics: |
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- type: Accuracy |
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value: 53.99 |
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- task: |
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type: Coreference resolution |
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dataset: |
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type: Muennighoff/xwinograd |
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name: XWinograd (ru) |
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config: ru |
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split: test |
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revision: 9dd5ea5505fad86b7bedad667955577815300cee |
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metrics: |
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- type: Accuracy |
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value: 53.97 |
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- task: |
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type: Coreference resolution |
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dataset: |
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type: Muennighoff/xwinograd |
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name: XWinograd (zh) |
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config: zh |
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split: test |
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revision: 9dd5ea5505fad86b7bedad667955577815300cee |
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metrics: |
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- type: Accuracy |
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value: 54.76 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: anli |
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name: ANLI (r1) |
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config: r1 |
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split: validation |
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revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 |
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metrics: |
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- type: Accuracy |
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value: 33.4 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: anli |
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name: ANLI (r2) |
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config: r2 |
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split: validation |
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revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 |
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metrics: |
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- type: Accuracy |
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value: 33.4 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: anli |
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name: ANLI (r3) |
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config: r3 |
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split: validation |
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revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 |
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metrics: |
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- type: Accuracy |
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value: 33.5 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: super_glue |
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name: SuperGLUE (cb) |
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config: cb |
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split: validation |
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revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 |
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metrics: |
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- type: Accuracy |
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value: 53.57 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: super_glue |
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name: SuperGLUE (rte) |
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config: rte |
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split: validation |
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revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 |
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metrics: |
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- type: Accuracy |
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value: 67.15 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (ar) |
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config: ar |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 44.46 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (bg) |
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config: bg |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 39.76 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (de) |
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config: de |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 39.36 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (el) |
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config: el |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 40.96 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (en) |
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config: en |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 46.43 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (es) |
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config: es |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 44.98 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (fr) |
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config: fr |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 45.54 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (hi) |
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config: hi |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 41.81 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (ru) |
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config: ru |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 39.64 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (sw) |
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config: sw |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 38.35 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (th) |
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config: th |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 35.5 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (tr) |
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config: tr |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 37.31 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (ur) |
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config: ur |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 38.96 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (vi) |
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config: vi |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 44.74 |
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- task: |
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type: Natural language inference |
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dataset: |
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type: xnli |
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name: XNLI (zh) |
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config: zh |
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split: validation |
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revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
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metrics: |
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- type: Accuracy |
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value: 44.66 |
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- task: |
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type: Program synthesis |
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dataset: |
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type: openai_humaneval |
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name: HumanEval |
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config: None |
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split: test |
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revision: e8dc562f5de170c54b5481011dd9f4fa04845771 |
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metrics: |
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- type: Pass@1 |
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value: 2.18 |
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- type: Pass@10 |
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value: 4.11 |
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- type: Pass@100 |
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value: 9.00 |
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- task: |
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type: Sentence completion |
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dataset: |
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type: story_cloze |
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name: StoryCloze (2016) |
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config: "2016" |
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split: validation |
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revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db |
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metrics: |
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- type: Accuracy |
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value: 60.29 |
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- task: |
|
type: Sentence completion |
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dataset: |
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type: super_glue |
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name: SuperGLUE (copa) |
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config: copa |
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split: validation |
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revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 |
|
metrics: |
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- type: Accuracy |
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value: 52.0 |
|
- task: |
|
type: Sentence completion |
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dataset: |
|
type: xcopa |
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name: XCOPA (et) |
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config: et |
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split: validation |
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revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
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- type: Accuracy |
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value: 53.0 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: xcopa |
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name: XCOPA (ht) |
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config: ht |
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split: validation |
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revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
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- type: Accuracy |
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value: 49.0 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: xcopa |
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name: XCOPA (id) |
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config: id |
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split: validation |
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revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
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- type: Accuracy |
|
value: 57.0 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: xcopa |
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name: XCOPA (it) |
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config: it |
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split: validation |
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revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
|
- type: Accuracy |
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value: 52.0 |
|
- task: |
|
type: Sentence completion |
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dataset: |
|
type: xcopa |
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name: XCOPA (qu) |
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config: qu |
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split: validation |
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revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
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metrics: |
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- type: Accuracy |
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value: 55.0 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: xcopa |
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name: XCOPA (sw) |
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config: sw |
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split: validation |
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revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
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- type: Accuracy |
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value: 56.0 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: xcopa |
|
name: XCOPA (ta) |
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config: ta |
|
split: validation |
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revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
|
- type: Accuracy |
|
value: 58.0 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: xcopa |
|
name: XCOPA (th) |
|
config: th |
|
split: validation |
|
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
|
- type: Accuracy |
|
value: 58.0 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: xcopa |
|
name: XCOPA (tr) |
|
config: tr |
|
split: validation |
|
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
|
- type: Accuracy |
|
value: 61.0 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: xcopa |
|
name: XCOPA (vi) |
|
config: vi |
|
split: validation |
|
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
|
- type: Accuracy |
|
value: 61.0 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: xcopa |
|
name: XCOPA (zh) |
|
config: zh |
|
split: validation |
|
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
|
- type: Accuracy |
|
value: 61.0 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: Muennighoff/xstory_cloze |
|
name: XStoryCloze (ar) |
|
config: ar |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 54.4 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: Muennighoff/xstory_cloze |
|
name: XStoryCloze (es) |
|
config: es |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 56.45 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: Muennighoff/xstory_cloze |
|
name: XStoryCloze (eu) |
|
config: eu |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 50.56 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: Muennighoff/xstory_cloze |
|
name: XStoryCloze (hi) |
|
config: hi |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 55.79 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: Muennighoff/xstory_cloze |
|
name: XStoryCloze (id) |
|
config: id |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 57.84 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: Muennighoff/xstory_cloze |
|
name: XStoryCloze (my) |
|
config: my |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 47.05 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: Muennighoff/xstory_cloze |
|
name: XStoryCloze (ru) |
|
config: ru |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 53.14 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: Muennighoff/xstory_cloze |
|
name: XStoryCloze (sw) |
|
config: sw |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 51.36 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: Muennighoff/xstory_cloze |
|
name: XStoryCloze (te) |
|
config: te |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 54.86 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
type: Muennighoff/xstory_cloze |
|
name: XStoryCloze (zh) |
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config: zh |
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split: validation |
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revision: 8bb76e594b68147f1a430e86829d07189622b90d |
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metrics: |
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- type: Accuracy |
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value: 56.52 |
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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. [Limitations](#limitations) |
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4. [Training](#training) |
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5. [Evaluation](#evaluation) |
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7. [Citation](#citation) |
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# Model Summary |
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> We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages. |
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- **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf) |
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- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) |
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- **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co) |
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- **Languages:** Refer to [bloom](https://huggingface.co/bigscience/bloom) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages. |
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- **BLOOMZ & mT0 Model Family:** |
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<div class="max-w-full overflow-auto"> |
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<table> |
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<tr> |
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<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English. |
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</tr> |
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<tr> |
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<td>Parameters</td> |
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<td>300M</td> |
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<td>580M</td> |
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<td>1.2B</td> |
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<td>3.7B</td> |
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<td>13B</td> |
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<td>560M</td> |
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<td>1.1B</td> |
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<td>1.7B</td> |
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<td>3B</td> |
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<td>7.1B</td> |
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<td>176B</td> |
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</tr> |
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<tr> |
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<td>Finetuned Model</td> |
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<td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td> |
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<td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td> |
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<td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td> |
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<td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td> |
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<td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> |
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</tr> |
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</tr> |
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<tr> |
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<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th> |
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</tr> |
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<tr> |
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<td>Finetuned Model</td> |
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<td></td> |
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<td></td> |
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<td></td> |
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<td></td> |
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<td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> |
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<td></td> |
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<td></td> |
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<td></td> |
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<td></td> |
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<td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td> |
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</tr> |
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<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th> |
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</tr> |
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<tr> |
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<td>Finetuned Model</td> |
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<td></td> |
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<td></td> |
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<td></td> |
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<td></td> |
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<td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> |
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<td></td> |
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<td></td> |
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<td></td> |
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<td></td> |
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<td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td> |
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</tr> |
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<th colspan="12">Original pretrained checkpoints. Not recommended.</th> |
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<tr> |
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<td>Pretrained Model</td> |
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<td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td> |
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<td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td> |
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<td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td> |
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<td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td> |
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<td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td> |
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<td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td> |
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</tr> |
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</table> |
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</div> |
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# Use |
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## Intended use |
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We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper: |
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- 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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- Suggest at least five related search terms to "Mạng neural nhân tạo". |
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- 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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- Explain in a sentence in Telugu what is backpropagation in neural networks. |
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**Feel free to share your generations in the Community tab!** |
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## How to use |
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### CPU |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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# pip install -q transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "bigscience/bloomz-560m" |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint) |
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inputs = tokenizer.encode("Translate to English: Je t’aime.", 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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</details> |
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### GPU |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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# pip install -q transformers accelerate |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "bigscience/bloomz-560m" |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto") |
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inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") |
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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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</details> |
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### GPU in 8bit |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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# pip install -q transformers accelerate bitsandbytes |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "bigscience/bloomz-560m" |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True) |
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inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") |
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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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</details> |
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<!-- Necessary for whitespace --> |
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### |
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# Limitations |
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**Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*". |
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# Training |
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## Model |
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- **Architecture:** Same as [bloom-560m](https://huggingface.co/bigscience/bloom-560m), also refer to the `config.json` file |
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- **Finetuning steps:** 1750 |
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- **Finetuning tokens:** 3.67 billion |
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- **Finetuning layout:** 1x pipeline parallel, 1x tensor parallel, 1x data parallel |
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- **Precision:** float16 |
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## Hardware |
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- **CPUs:** AMD CPUs with 512GB memory per node |
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- **GPUs:** 64 A100 80GB GPUs with 8 GPUs per node (8 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links |
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- **Communication:** NCCL-communications network with a fully dedicated subnet |
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## Software |
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- **Orchestration:** [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed) |
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- **Optimizer & parallelism:** [DeepSpeed](https://github.com/microsoft/DeepSpeed) |
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- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) (pytorch-1.11 w/ CUDA-11.5) |
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- **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex) |
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# Evaluation |
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We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config. |
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# Citation |
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```bibtex |
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@misc{muennighoff2022crosslingual, |
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title={Crosslingual Generalization through Multitask Finetuning}, |
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author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, |
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year={2022}, |
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eprint={2211.01786}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
