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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 |
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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. |
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The fairy tale is a masterpiece that has achieved praise worldwide and its moral |
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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 |
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by ogres. The fable is a masterpiece that has achieved praise worldwide and its |
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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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tags: |
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- TensorBlock |
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- GGUF |
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base_model: bigscience/bloomz-3b |
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model-index: |
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- name: bloomz-3b1 |
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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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name: Winogrande XL (xl) |
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type: winogrande |
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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: 53.67 |
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- task: |
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type: Coreference resolution |
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dataset: |
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name: XWinograd (en) |
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type: Muennighoff/xwinograd |
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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: 59.23 |
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- task: |
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type: Coreference resolution |
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dataset: |
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name: XWinograd (fr) |
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type: Muennighoff/xwinograd |
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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: 53.01 |
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- task: |
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type: Coreference resolution |
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dataset: |
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name: XWinograd (jp) |
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type: Muennighoff/xwinograd |
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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: 52.45 |
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- task: |
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type: Coreference resolution |
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dataset: |
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name: XWinograd (pt) |
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type: Muennighoff/xwinograd |
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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: 53.61 |
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- task: |
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type: Coreference resolution |
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dataset: |
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name: XWinograd (ru) |
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type: Muennighoff/xwinograd |
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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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name: XWinograd (zh) |
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type: Muennighoff/xwinograd |
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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: 60.91 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: ANLI (r1) |
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type: anli |
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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: 40.1 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: ANLI (r2) |
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type: anli |
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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: 36.8 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: ANLI (r3) |
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type: anli |
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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: 40.0 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: SuperGLUE (cb) |
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type: super_glue |
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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: 75.0 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: SuperGLUE (rte) |
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type: super_glue |
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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: 76.17 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (ar) |
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type: xnli |
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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: 53.29 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (bg) |
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type: xnli |
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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: 43.82 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (de) |
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type: xnli |
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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: 45.26 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (el) |
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type: xnli |
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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: 42.61 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (en) |
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type: xnli |
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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: 57.31 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (es) |
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type: xnli |
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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: 56.14 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (fr) |
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type: xnli |
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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: 55.78 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (hi) |
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type: xnli |
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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: 51.49 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (ru) |
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type: xnli |
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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: 47.11 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (sw) |
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type: xnli |
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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: 47.83 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (th) |
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type: xnli |
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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: 42.93 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (tr) |
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type: xnli |
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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: 37.23 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (ur) |
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type: xnli |
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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: 49.04 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (vi) |
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type: xnli |
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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: 53.98 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (zh) |
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type: xnli |
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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: 54.18 |
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- task: |
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type: Program synthesis |
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dataset: |
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name: HumanEval |
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type: openai_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: 6.29 |
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- type: Pass@10 |
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value: 11.94 |
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- type: Pass@100 |
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value: 19.06 |
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- task: |
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type: Sentence completion |
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dataset: |
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name: StoryCloze (2016) |
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type: story_cloze |
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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: 87.33 |
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- task: |
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type: Sentence completion |
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dataset: |
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name: SuperGLUE (copa) |
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type: super_glue |
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config: copa |
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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: 76.0 |
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- task: |
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type: Sentence completion |
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dataset: |
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name: XCOPA (et) |
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type: xcopa |
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config: et |
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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: 53.0 |
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- task: |
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type: Sentence completion |
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dataset: |
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name: XCOPA (ht) |
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type: xcopa |
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config: ht |
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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: 64.0 |
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- task: |
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type: Sentence completion |
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dataset: |
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name: XCOPA (id) |
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type: xcopa |
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config: id |
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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: 70.0 |
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- task: |
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type: Sentence completion |
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dataset: |
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name: XCOPA (it) |
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type: xcopa |
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config: it |
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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: 53.0 |
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- task: |
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type: Sentence completion |
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dataset: |
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name: XCOPA (qu) |
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type: xcopa |
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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: 56.0 |
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- task: |
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type: Sentence completion |
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dataset: |
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name: XCOPA (sw) |
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type: xcopa |
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config: sw |
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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: 66.0 |
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- task: |
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type: Sentence completion |
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dataset: |
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name: XCOPA (ta) |
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type: xcopa |
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config: ta |
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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: 59.0 |
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- task: |
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type: Sentence completion |
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dataset: |
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name: XCOPA (th) |
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type: xcopa |
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config: th |
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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: 63.0 |
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- task: |
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type: Sentence completion |
|
dataset: |
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name: XCOPA (tr) |
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type: xcopa |
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config: tr |
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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: 61.0 |
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- task: |
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type: Sentence completion |
|
dataset: |
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name: XCOPA (vi) |
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type: xcopa |
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config: vi |
|
split: validation |
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revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
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- type: Accuracy |
|
value: 77.0 |
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- task: |
|
type: Sentence completion |
|
dataset: |
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name: XCOPA (zh) |
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type: xcopa |
|
config: zh |
|
split: validation |
|
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
|
- type: Accuracy |
|
value: 73.0 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
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name: XStoryCloze (ar) |
|
type: Muennighoff/xstory_cloze |
|
config: ar |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 80.61 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
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name: XStoryCloze (es) |
|
type: Muennighoff/xstory_cloze |
|
config: es |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 85.9 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
name: XStoryCloze (eu) |
|
type: Muennighoff/xstory_cloze |
|
config: eu |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 70.95 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
name: XStoryCloze (hi) |
|
type: Muennighoff/xstory_cloze |
|
config: hi |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 78.89 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
name: XStoryCloze (id) |
|
type: Muennighoff/xstory_cloze |
|
config: id |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 82.99 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
name: XStoryCloze (my) |
|
type: Muennighoff/xstory_cloze |
|
config: my |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 49.9 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
name: XStoryCloze (ru) |
|
type: Muennighoff/xstory_cloze |
|
config: ru |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 61.42 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
name: XStoryCloze (sw) |
|
type: Muennighoff/xstory_cloze |
|
config: sw |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 69.69 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
name: XStoryCloze (te) |
|
type: Muennighoff/xstory_cloze |
|
config: te |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 73.66 |
|
- task: |
|
type: Sentence completion |
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dataset: |
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name: XStoryCloze (zh) |
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type: Muennighoff/xstory_cloze |
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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: 84.32 |
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--- |
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<div style="width: auto; margin-left: auto; margin-right: auto"> |
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<div style="display: flex; justify-content: space-between; width: 100%;"> |
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<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
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<p style="margin-top: 0.5em; margin-bottom: 0em;"> |
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Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> |
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</p> |
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</div> |
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</div> |
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## bigscience/bloomz-3b - GGUF |
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This repo contains GGUF format model files for [bigscience/bloomz-3b](https://huggingface.co/bigscience/bloomz-3b). |
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The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). |
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<div style="text-align: left; margin: 20px 0;"> |
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<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> |
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Run them on the TensorBlock client using your local machine ↗ |
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</a> |
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</div> |
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## Prompt template |
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``` |
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``` |
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## Model file specification |
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| Filename | Quant type | File Size | Description | |
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| -------- | ---------- | --------- | ----------- | |
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| [bloomz-3b-Q2_K.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q2_K.gguf) | Q2_K | 1.516 GB | smallest, significant quality loss - not recommended for most purposes | |
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| [bloomz-3b-Q3_K_S.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q3_K_S.gguf) | Q3_K_S | 1.707 GB | very small, high quality loss | |
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| [bloomz-3b-Q3_K_M.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q3_K_M.gguf) | Q3_K_M | 1.905 GB | very small, high quality loss | |
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| [bloomz-3b-Q3_K_L.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q3_K_L.gguf) | Q3_K_L | 2.016 GB | small, substantial quality loss | |
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| [bloomz-3b-Q4_0.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q4_0.gguf) | Q4_0 | 2.079 GB | legacy; small, very high quality loss - prefer using Q3_K_M | |
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| [bloomz-3b-Q4_K_S.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q4_K_S.gguf) | Q4_K_S | 2.088 GB | small, greater quality loss | |
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| [bloomz-3b-Q4_K_M.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q4_K_M.gguf) | Q4_K_M | 2.235 GB | medium, balanced quality - recommended | |
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| [bloomz-3b-Q5_0.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q5_0.gguf) | Q5_0 | 2.428 GB | legacy; medium, balanced quality - prefer using Q4_K_M | |
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| [bloomz-3b-Q5_K_S.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q5_K_S.gguf) | Q5_K_S | 2.428 GB | large, low quality loss - recommended | |
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| [bloomz-3b-Q5_K_M.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q5_K_M.gguf) | Q5_K_M | 2.546 GB | large, very low quality loss - recommended | |
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| [bloomz-3b-Q6_K.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q6_K.gguf) | Q6_K | 2.799 GB | very large, extremely low quality loss | |
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| [bloomz-3b-Q8_0.gguf](https://huggingface.co/tensorblock/bloomz-3b-GGUF/blob/main/bloomz-3b-Q8_0.gguf) | Q8_0 | 3.621 GB | very large, extremely low quality loss - not recommended | |
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## Downloading instruction |
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### Command line |
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Firstly, install Huggingface Client |
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```shell |
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pip install -U "huggingface_hub[cli]" |
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``` |
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Then, downoad the individual model file the a local directory |
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```shell |
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huggingface-cli download tensorblock/bloomz-3b-GGUF --include "bloomz-3b-Q2_K.gguf" --local-dir MY_LOCAL_DIR |
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``` |
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: |
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```shell |
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huggingface-cli download tensorblock/bloomz-3b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' |
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``` |
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