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--- |
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datasets: |
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- bigscience/xP3mt |
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- mc4 |
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license: apache-2.0 |
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language: |
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- af |
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- am |
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- ar |
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- az |
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- be |
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- bg |
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- bn |
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- ca |
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- ceb |
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- co |
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- cs |
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- cy |
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- da |
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- de |
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- el |
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- en |
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- eo |
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- es |
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- et |
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- eu |
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- fa |
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- fi |
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- fil |
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- fr |
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- fy |
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- ga |
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- gd |
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- gl |
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- gu |
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- ha |
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- haw |
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- hi |
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- hmn |
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- ht |
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- hu |
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- hy |
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- ig |
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- is |
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- it |
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- iw |
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- ja |
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- jv |
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- ka |
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- kk |
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- km |
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- kn |
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- ko |
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- ku |
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- ky |
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- la |
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- lb |
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- lo |
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- lt |
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- lv |
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- mg |
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- mi |
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- mk |
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- ml |
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- mn |
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- mr |
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- ms |
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- mt |
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- my |
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- ne |
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- nl |
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- 'no' |
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- ny |
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- pa |
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- pl |
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- ps |
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- pt |
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- ro |
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- ru |
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- sd |
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- si |
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- sk |
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- sl |
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- sm |
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- sn |
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- so |
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- sq |
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- sr |
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- st |
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- su |
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- sv |
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- sw |
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- ta |
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- te |
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- tg |
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- th |
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- tr |
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- uk |
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- und |
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- ur |
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- uz |
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- vi |
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- xh |
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- yi |
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- yo |
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- zh |
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- zu |
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tags: |
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- text2text-generation |
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- llama-cpp |
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- gguf-my-repo |
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widget: |
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- text: Life is beautiful! Translate to Mongolian. |
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example_title: mn-en translation |
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- text: Le mot japonais «憂鬱» veut dire quoi en Odia? |
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example_title: jp-or-fr translation |
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- text: Stell mir eine schwierige Quiz Frage bei der es um Astronomie geht. Bitte |
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stell die Frage auf Norwegisch. |
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example_title: de-nb quiz |
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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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pipeline_tag: text2text-generation |
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base_model: bigscience/mt0-xxl-mt |
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model-index: |
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- name: mt0-xxl-mt |
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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: 62.67 |
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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: 83.31 |
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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: 78.31 |
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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: 80.19 |
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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: 80.99 |
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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: 79.05 |
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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: 82.34 |
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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: 49.5 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: ANLI (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: 42 |
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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: 48.17 |
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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: 87.5 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: SuperGLUE (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: 84.84 |
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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: 58.03 |
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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: 59.92 |
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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: 60.16 |
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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: 59.2 |
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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: 62.25 |
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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: 60.92 |
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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: 59.88 |
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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: 57.47 |
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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: 58.67 |
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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: 56.79 |
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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: 58.03 |
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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: 57.67 |
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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: 55.98 |
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- task: |
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type: Natural language inference |
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dataset: |
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name: XNLI (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: 58.92 |
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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: 58.71 |
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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: 94.66 |
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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: 88 |
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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: 81 |
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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: 79 |
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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: 90 |
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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: 88 |
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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 |
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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: 81 |
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- task: |
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type: Sentence completion |
|
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 |
|
metrics: |
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- type: Accuracy |
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value: 81 |
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- task: |
|
type: Sentence completion |
|
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 |
|
metrics: |
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- type: Accuracy |
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value: 76 |
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- task: |
|
type: Sentence completion |
|
dataset: |
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name: XCOPA (tr) |
|
type: xcopa |
|
config: tr |
|
split: validation |
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revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
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- type: Accuracy |
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value: 76 |
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- task: |
|
type: Sentence completion |
|
dataset: |
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name: XCOPA (vi) |
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type: xcopa |
|
config: vi |
|
split: validation |
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revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
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- type: Accuracy |
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value: 85 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
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name: XCOPA (zh) |
|
type: xcopa |
|
config: zh |
|
split: validation |
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revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
|
metrics: |
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- type: Accuracy |
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value: 87 |
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- task: |
|
type: Sentence completion |
|
dataset: |
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name: XStoryCloze (ar) |
|
type: Muennighoff/xstory_cloze |
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config: ar |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 91 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
name: XStoryCloze (es) |
|
type: Muennighoff/xstory_cloze |
|
config: es |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 93.38 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
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name: XStoryCloze (eu) |
|
type: Muennighoff/xstory_cloze |
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config: eu |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
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- type: Accuracy |
|
value: 91.13 |
|
- task: |
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type: Sentence completion |
|
dataset: |
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name: XStoryCloze (hi) |
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type: Muennighoff/xstory_cloze |
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config: hi |
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split: validation |
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revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
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- type: Accuracy |
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value: 90.73 |
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- task: |
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type: Sentence completion |
|
dataset: |
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name: XStoryCloze (id) |
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type: Muennighoff/xstory_cloze |
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config: id |
|
split: validation |
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revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
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- type: Accuracy |
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value: 93.05 |
|
- task: |
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type: Sentence completion |
|
dataset: |
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name: XStoryCloze (my) |
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type: Muennighoff/xstory_cloze |
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config: my |
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split: validation |
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revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 86.7 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
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name: XStoryCloze (ru) |
|
type: Muennighoff/xstory_cloze |
|
config: ru |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 91.66 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
name: XStoryCloze (sw) |
|
type: Muennighoff/xstory_cloze |
|
config: sw |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 89.61 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
name: XStoryCloze (te) |
|
type: Muennighoff/xstory_cloze |
|
config: te |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 90.4 |
|
- task: |
|
type: Sentence completion |
|
dataset: |
|
name: XStoryCloze (zh) |
|
type: Muennighoff/xstory_cloze |
|
config: zh |
|
split: validation |
|
revision: 8bb76e594b68147f1a430e86829d07189622b90d |
|
metrics: |
|
- type: Accuracy |
|
value: 93.05 |
|
--- |
|
|
|
# Markobes/mt0-xxl-mt-Q5_K_M-GGUF |
|
This model was converted to GGUF format from [`bigscience/mt0-xxl-mt`](https://huggingface.co/bigscience/mt0-xxl-mt) 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/mt0-xxl-mt) 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 Markobes/mt0-xxl-mt-Q5_K_M-GGUF --hf-file mt0-xxl-mt-q5_k_m.gguf -p "The meaning to life and the universe is" |
|
``` |
|
|
|
### Server: |
|
```bash |
|
llama-server --hf-repo Markobes/mt0-xxl-mt-Q5_K_M-GGUF --hf-file mt0-xxl-mt-q5_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 Markobes/mt0-xxl-mt-Q5_K_M-GGUF --hf-file mt0-xxl-mt-q5_k_m.gguf -p "The meaning to life and the universe is" |
|
``` |
|
or |
|
``` |
|
./llama-server --hf-repo Markobes/mt0-xxl-mt-Q5_K_M-GGUF --hf-file mt0-xxl-mt-q5_k_m.gguf -c 2048 |
|
``` |
|
|
