| --- |
| datasets: |
| - bigscience/xP3mt |
| - mc4 |
| license: apache-2.0 |
| language: |
| - af |
| - am |
| - ar |
| - az |
| - be |
| - bg |
| - bn |
| - ca |
| - ceb |
| - co |
| - cs |
| - cy |
| - da |
| - de |
| - el |
| - en |
| - eo |
| - es |
| - et |
| - eu |
| - fa |
| - fi |
| - fil |
| - fr |
| - fy |
| - ga |
| - gd |
| - gl |
| - gu |
| - ha |
| - haw |
| - hi |
| - hmn |
| - ht |
| - hu |
| - hy |
| - ig |
| - is |
| - it |
| - iw |
| - ja |
| - jv |
| - ka |
| - kk |
| - km |
| - kn |
| - ko |
| - ku |
| - ky |
| - la |
| - lb |
| - lo |
| - lt |
| - lv |
| - mg |
| - mi |
| - mk |
| - ml |
| - mn |
| - mr |
| - ms |
| - mt |
| - my |
| - ne |
| - nl |
| - 'no' |
| - ny |
| - pa |
| - pl |
| - ps |
| - pt |
| - ro |
| - ru |
| - sd |
| - si |
| - sk |
| - sl |
| - sm |
| - sn |
| - so |
| - sq |
| - sr |
| - st |
| - su |
| - sv |
| - sw |
| - ta |
| - te |
| - tg |
| - th |
| - tr |
| - uk |
| - und |
| - ur |
| - uz |
| - vi |
| - xh |
| - yi |
| - yo |
| - zh |
| - zu |
| tags: |
| - text2text-generation |
| - llama-cpp |
| - gguf-my-repo |
| widget: |
| - text: Life is beautiful! Translate to Mongolian. |
| example_title: mn-en translation |
| - text: Le mot japonais «憂鬱» veut dire quoi en Odia? |
| example_title: jp-or-fr translation |
| - text: Stell mir eine schwierige Quiz Frage bei der es um Astronomie geht. Bitte |
| stell die Frage auf Norwegisch. |
| example_title: de-nb quiz |
| - text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous |
| review as positive, neutral or negative? |
| example_title: zh-en sentiment |
| - 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？ |
| example_title: zh-zh sentiment |
| - text: Suggest at least five related search terms to "Mạng neural nhân tạo". |
| example_title: vi-en query |
| - text: Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels». |
| example_title: fr-fr query |
| - text: Explain in a sentence in Telugu what is backpropagation in neural networks. |
| example_title: te-en qa |
| - text: Why is the sky blue? |
| example_title: en-en qa |
| - text: 'Write a fairy tale about a troll saving a princess from a dangerous dragon. |
| The fairy tale is a masterpiece that has achieved praise worldwide and its moral |
| is "Heroes Come in All Shapes and Sizes". Story (in Spanish):' |
| example_title: es-en fable |
| - text: 'Write a fable about wood elves living in a forest that is suddenly invaded |
| by ogres. The fable is a masterpiece that has achieved praise worldwide and its |
| moral is "Violence is the last refuge of the incompetent". Fable (in Hindi):' |
| example_title: hi-en fable |
| pipeline_tag: text2text-generation |
| base_model: bigscience/mt0-xxl-mt |
| model-index: |
| - name: mt0-xxl-mt |
| results: |
| - task: |
| type: Coreference resolution |
| dataset: |
| name: Winogrande XL (xl) |
| type: winogrande |
| config: xl |
| split: validation |
| revision: a80f460359d1e9a67c006011c94de42a8759430c |
| metrics: |
| - type: Accuracy |
| value: 62.67 |
| - task: |
| type: Coreference resolution |
| dataset: |
| name: XWinograd (en) |
| type: Muennighoff/xwinograd |
| config: en |
| split: test |
| revision: 9dd5ea5505fad86b7bedad667955577815300cee |
| metrics: |
| - type: Accuracy |
| value: 83.31 |
| - task: |
| type: Coreference resolution |
| dataset: |
| name: XWinograd (fr) |
| type: Muennighoff/xwinograd |
| config: fr |
| split: test |
| revision: 9dd5ea5505fad86b7bedad667955577815300cee |
| metrics: |
| - type: Accuracy |
| value: 78.31 |
| - task: |
| type: Coreference resolution |
| dataset: |
| name: XWinograd (jp) |
| type: Muennighoff/xwinograd |
| config: jp |
| split: test |
| revision: 9dd5ea5505fad86b7bedad667955577815300cee |
| metrics: |
| - type: Accuracy |
| value: 80.19 |
| - task: |
| type: Coreference resolution |
| dataset: |
| name: XWinograd (pt) |
| type: Muennighoff/xwinograd |
| config: pt |
| split: test |
| revision: 9dd5ea5505fad86b7bedad667955577815300cee |
| metrics: |
| - type: Accuracy |
| value: 80.99 |
| - task: |
| type: Coreference resolution |
| dataset: |
| name: XWinograd (ru) |
| type: Muennighoff/xwinograd |
| config: ru |
| split: test |
| revision: 9dd5ea5505fad86b7bedad667955577815300cee |
| metrics: |
| - type: Accuracy |
| value: 79.05 |
| - task: |
| type: Coreference resolution |
| dataset: |
| name: XWinograd (zh) |
| type: Muennighoff/xwinograd |
| config: zh |
| split: test |
| revision: 9dd5ea5505fad86b7bedad667955577815300cee |
| metrics: |
| - type: Accuracy |
| value: 82.34 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: ANLI (r1) |
| type: anli |
| config: r1 |
| split: validation |
| revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 |
| metrics: |
| - type: Accuracy |
| value: 49.5 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: ANLI (r2) |
| type: anli |
| config: r2 |
| split: validation |
| revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 |
| metrics: |
| - type: Accuracy |
| value: 42 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: ANLI (r3) |
| type: anli |
| config: r3 |
| split: validation |
| revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 |
| metrics: |
| - type: Accuracy |
| value: 48.17 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: SuperGLUE (cb) |
| type: super_glue |
| config: cb |
| split: validation |
| revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 |
| metrics: |
| - type: Accuracy |
| value: 87.5 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: SuperGLUE (rte) |
| type: super_glue |
| config: rte |
| split: validation |
| revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 |
| metrics: |
| - type: Accuracy |
| value: 84.84 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (ar) |
| type: xnli |
| config: ar |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 58.03 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (bg) |
| type: xnli |
| config: bg |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 59.92 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (de) |
| type: xnli |
| config: de |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 60.16 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (el) |
| type: xnli |
| config: el |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 59.2 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (en) |
| type: xnli |
| config: en |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 62.25 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (es) |
| type: xnli |
| config: es |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 60.92 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (fr) |
| type: xnli |
| config: fr |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 59.88 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (hi) |
| type: xnli |
| config: hi |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 57.47 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (ru) |
| type: xnli |
| config: ru |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 58.67 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (sw) |
| type: xnli |
| config: sw |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 56.79 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (th) |
| type: xnli |
| config: th |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 58.03 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (tr) |
| type: xnli |
| config: tr |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 57.67 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (ur) |
| type: xnli |
| config: ur |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 55.98 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (vi) |
| type: xnli |
| config: vi |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 58.92 |
| - task: |
| type: Natural language inference |
| dataset: |
| name: XNLI (zh) |
| type: xnli |
| config: zh |
| split: validation |
| revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 |
| metrics: |
| - type: Accuracy |
| value: 58.71 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: StoryCloze (2016) |
| type: story_cloze |
| config: '2016' |
| split: validation |
| revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db |
| metrics: |
| - type: Accuracy |
| value: 94.66 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: SuperGLUE (copa) |
| type: super_glue |
| config: copa |
| split: validation |
| revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 |
| metrics: |
| - type: Accuracy |
| value: 88 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XCOPA (et) |
| type: xcopa |
| config: et |
| split: validation |
| revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
| metrics: |
| - type: Accuracy |
| value: 81 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XCOPA (ht) |
| type: xcopa |
| config: ht |
| split: validation |
| revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
| metrics: |
| - type: Accuracy |
| value: 79 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XCOPA (id) |
| type: xcopa |
| config: id |
| split: validation |
| revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
| metrics: |
| - type: Accuracy |
| value: 90 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XCOPA (it) |
| type: xcopa |
| config: it |
| split: validation |
| revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
| metrics: |
| - type: Accuracy |
| value: 88 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XCOPA (qu) |
| type: xcopa |
| config: qu |
| split: validation |
| revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
| metrics: |
| - type: Accuracy |
| value: 56 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XCOPA (sw) |
| type: xcopa |
| config: sw |
| split: validation |
| revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
| metrics: |
| - type: Accuracy |
| value: 81 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XCOPA (ta) |
| type: xcopa |
| config: ta |
| split: validation |
| revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
| metrics: |
| - type: Accuracy |
| value: 81 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XCOPA (th) |
| type: xcopa |
| config: th |
| split: validation |
| revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
| metrics: |
| - type: Accuracy |
| value: 76 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XCOPA (tr) |
| type: xcopa |
| config: tr |
| split: validation |
| revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
| metrics: |
| - type: Accuracy |
| value: 76 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XCOPA (vi) |
| type: xcopa |
| config: vi |
| split: validation |
| revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
| metrics: |
| - type: Accuracy |
| value: 85 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XCOPA (zh) |
| type: xcopa |
| config: zh |
| split: validation |
| revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 |
| metrics: |
| - type: Accuracy |
| value: 87 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XStoryCloze (ar) |
| type: Muennighoff/xstory_cloze |
| 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: |
| name: XStoryCloze (eu) |
| type: Muennighoff/xstory_cloze |
| config: eu |
| split: validation |
| revision: 8bb76e594b68147f1a430e86829d07189622b90d |
| metrics: |
| - type: Accuracy |
| value: 91.13 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XStoryCloze (hi) |
| type: Muennighoff/xstory_cloze |
| config: hi |
| split: validation |
| revision: 8bb76e594b68147f1a430e86829d07189622b90d |
| metrics: |
| - type: Accuracy |
| value: 90.73 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XStoryCloze (id) |
| type: Muennighoff/xstory_cloze |
| config: id |
| split: validation |
| revision: 8bb76e594b68147f1a430e86829d07189622b90d |
| metrics: |
| - type: Accuracy |
| value: 93.05 |
| - task: |
| type: Sentence completion |
| dataset: |
| name: XStoryCloze (my) |
| type: Muennighoff/xstory_cloze |
| config: my |
| split: validation |
| revision: 8bb76e594b68147f1a430e86829d07189622b90d |
| metrics: |
| - type: Accuracy |
| value: 86.7 |
| - task: |
| type: Sentence completion |
| dataset: |
| 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-Q4_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-Q4_K_M-GGUF --hf-file mt0-xxl-mt-q4_k_m.gguf -p "The meaning to life and the universe is" |
| ``` |
|
|
| ### Server: |
| ```bash |
| llama-server --hf-repo Markobes/mt0-xxl-mt-Q4_K_M-GGUF --hf-file mt0-xxl-mt-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 Markobes/mt0-xxl-mt-Q4_K_M-GGUF --hf-file mt0-xxl-mt-q4_k_m.gguf -p "The meaning to life and the universe is" |
| ``` |
| or |
| ``` |
| ./llama-server --hf-repo Markobes/mt0-xxl-mt-Q4_K_M-GGUF --hf-file mt0-xxl-mt-q4_k_m.gguf -c 2048 |
| ``` |
| |
