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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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- llama-cpp |
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- gguf-my-repo |
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base_model: bigscience/bloomz-1b1 |
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model-index: |
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- name: bloomz-1b1 |
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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: 52.33 |
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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: 50.49 |
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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: 59.04 |
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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: 51.82 |
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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: 54.75 |
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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: 55.16 |
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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: 33.3 |
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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: 33.5 |
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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: 34.5 |
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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: 58.93 |
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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: 65.7 |
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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: 46.59 |
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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: 40.4 |
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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: 40.12 |
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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: 39.32 |
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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: 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 (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: 47.55 |
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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: 48.51 |
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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: 42.89 |
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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: 42.81 |
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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: 41.29 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (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: 37.51 |
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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: 41.37 |
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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: 47.19 |
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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: 47.63 |
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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: 2.62 |
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- type: Pass@10 |
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value: 6.22 |
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- type: Pass@100 |
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value: 11.68 |
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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: 62.75 |
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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: 63.0 |
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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: 55.0 |
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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: 52.0 |
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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: 60.0 |
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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: 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 (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: 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 (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: 57.0 |
|
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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: 59.0 |
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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: |
|
|
- type: Accuracy |
|
|
value: 55.0 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XCOPA (vi) |
|
|
type: xcopa |
|
|
config: vi |
|
|
split: validation |
|
|
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 63.0 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XCOPA (zh) |
|
|
type: xcopa |
|
|
config: zh |
|
|
split: validation |
|
|
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 61.0 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XStoryCloze (ar) |
|
|
type: Muennighoff/xstory_cloze |
|
|
config: ar |
|
|
split: validation |
|
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 53.54 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XStoryCloze (es) |
|
|
type: Muennighoff/xstory_cloze |
|
|
config: es |
|
|
split: validation |
|
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 58.37 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XStoryCloze (eu) |
|
|
type: Muennighoff/xstory_cloze |
|
|
config: eu |
|
|
split: validation |
|
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 52.35 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XStoryCloze (hi) |
|
|
type: Muennighoff/xstory_cloze |
|
|
config: hi |
|
|
split: validation |
|
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 55.92 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XStoryCloze (id) |
|
|
type: Muennighoff/xstory_cloze |
|
|
config: id |
|
|
split: validation |
|
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 57.97 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XStoryCloze (my) |
|
|
type: Muennighoff/xstory_cloze |
|
|
config: my |
|
|
split: validation |
|
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 47.05 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XStoryCloze (ru) |
|
|
type: Muennighoff/xstory_cloze |
|
|
config: ru |
|
|
split: validation |
|
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 50.3 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XStoryCloze (sw) |
|
|
type: Muennighoff/xstory_cloze |
|
|
config: sw |
|
|
split: validation |
|
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 49.97 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XStoryCloze (te) |
|
|
type: Muennighoff/xstory_cloze |
|
|
config: te |
|
|
split: validation |
|
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 55.86 |
|
|
- task: |
|
|
type: Sentence completion |
|
|
dataset: |
|
|
name: XStoryCloze (zh) |
|
|
type: Muennighoff/xstory_cloze |
|
|
config: zh |
|
|
split: validation |
|
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
|
metrics: |
|
|
- type: Accuracy |
|
|
value: 58.17 |
|
|
--- |
|
|
|
|
|
# ahashan-habib-eatl/bloomz-1b1-Q4_K_M-GGUF |
|
|
This model was converted to GGUF format from [`bigscience/bloomz-1b1`](https://huggingface.co/bigscience/bloomz-1b1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. |
|
|
Refer to the [original model card](https://huggingface.co/bigscience/bloomz-1b1) for more details on the model. |
|
|
|
|
|
## Use with llama.cpp |
|
|
Install llama.cpp through brew (works on Mac and Linux) |
|
|
|
|
|
```bash |
|
|
brew install llama.cpp |
|
|
|
|
|
``` |
|
|
Invoke the llama.cpp server or the CLI. |
|
|
|
|
|
### CLI: |
|
|
```bash |
|
|
llama-cli --hf-repo ahashan-habib-eatl/bloomz-1b1-Q4_K_M-GGUF --hf-file bloomz-1b1-q4_k_m.gguf -p "The meaning to life and the universe is" |
|
|
``` |
|
|
|
|
|
### Server: |
|
|
```bash |
|
|
llama-server --hf-repo ahashan-habib-eatl/bloomz-1b1-Q4_K_M-GGUF --hf-file bloomz-1b1-q4_k_m.gguf -c 2048 |
|
|
``` |
|
|
|
|
|
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
|
|
|
|
|
Step 1: Clone llama.cpp from GitHub. |
|
|
``` |
|
|
git clone https://github.com/ggerganov/llama.cpp |
|
|
``` |
|
|
|
|
|
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). |
|
|
``` |
|
|
cd llama.cpp && LLAMA_CURL=1 make |
|
|
``` |
|
|
|
|
|
Step 3: Run inference through the main binary. |
|
|
``` |
|
|
./llama-cli --hf-repo ahashan-habib-eatl/bloomz-1b1-Q4_K_M-GGUF --hf-file bloomz-1b1-q4_k_m.gguf -p "The meaning to life and the universe is" |
|
|
``` |
|
|
or |
|
|
``` |
|
|
./llama-server --hf-repo ahashan-habib-eatl/bloomz-1b1-Q4_K_M-GGUF --hf-file bloomz-1b1-q4_k_m.gguf -c 2048 |
|
|
``` |
|
|
|
