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README.md

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+---
+datasets:
+- bigscience/xP3
+- 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
+pipeline_tag: text2text-generation
+widget:
+- 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
+tags:
+- llama-cpp
+- gguf-my-repo
+base_model: bigscience/mt0-large
+model-index:
+- name: mt0-large
+  results:
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: Winogrande XL (xl)
+      type: winogrande
+      config: xl
+      split: validation
+      revision: a80f460359d1e9a67c006011c94de42a8759430c
+    metrics:
+    - type: Accuracy
+      value: 51.78
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (en)
+      type: Muennighoff/xwinograd
+      config: en
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 54.8
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (fr)
+      type: Muennighoff/xwinograd
+      config: fr
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 56.63
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (jp)
+      type: Muennighoff/xwinograd
+      config: jp
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 53.08
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (pt)
+      type: Muennighoff/xwinograd
+      config: pt
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 56.27
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (ru)
+      type: Muennighoff/xwinograd
+      config: ru
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 55.56
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (zh)
+      type: Muennighoff/xwinograd
+      config: zh
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 54.37
+  - task:
+      type: Natural language inference
+    dataset:
+      name: ANLI (r1)
+      type: anli
+      config: r1
+      split: validation
+      revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
+    metrics:
+    - type: Accuracy
+      value: 33.3
+  - task:
+      type: Natural language inference
+    dataset:
+      name: ANLI (r2)
+      type: anli
+      config: r2
+      split: validation
+      revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
+    metrics:
+    - type: Accuracy
+      value: 34.7
+  - task:
+      type: Natural language inference
+    dataset:
+      name: ANLI (r3)
+      type: anli
+      config: r3
+      split: validation
+      revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
+    metrics:
+    - type: Accuracy
+      value: 34.75
+  - task:
+      type: Natural language inference
+    dataset:
+      name: SuperGLUE (cb)
+      type: super_glue
+      config: cb
+      split: validation
+      revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
+    metrics:
+    - type: Accuracy
+      value: 51.79
+  - task:
+      type: Natural language inference
+    dataset:
+      name: SuperGLUE (rte)
+      type: super_glue
+      config: rte
+      split: validation
+      revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
+    metrics:
+    - type: Accuracy
+      value: 64.26
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (ar)
+      type: xnli
+      config: ar
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 42.61
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (bg)
+      type: xnli
+      config: bg
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 43.94
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (de)
+      type: xnli
+      config: de
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 44.18
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (el)
+      type: xnli
+      config: el
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 43.94
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (en)
+      type: xnli
+      config: en
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 44.26
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (es)
+      type: xnli
+      config: es
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 45.34
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (fr)
+      type: xnli
+      config: fr
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 42.01
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (hi)
+      type: xnli
+      config: hi
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 41.89
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (ru)
+      type: xnli
+      config: ru
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 42.13
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (sw)
+      type: xnli
+      config: sw
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 40.08
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (th)
+      type: xnli
+      config: th
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 40.8
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (tr)
+      type: xnli
+      config: tr
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 41.29
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (ur)
+      type: xnli
+      config: ur
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 39.88
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (vi)
+      type: xnli
+      config: vi
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 41.81
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (zh)
+      type: xnli
+      config: zh
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 40.84
+  - task:
+      type: Sentence completion
+    dataset:
+      name: StoryCloze (2016)
+      type: story_cloze
+      config: '2016'
+      split: validation
+      revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
+    metrics:
+    - type: Accuracy
+      value: 59.49
+  - task:
+      type: Sentence completion
+    dataset:
+      name: SuperGLUE (copa)
+      type: super_glue
+      config: copa
+      split: validation
+      revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
+    metrics:
+    - type: Accuracy
+      value: 65
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (et)
+      type: xcopa
+      config: et
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 56
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (ht)
+      type: xcopa
+      config: ht
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 62
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (id)
+      type: xcopa
+      config: id
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 61
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (it)
+      type: xcopa
+      config: it
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 63
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (qu)
+      type: xcopa
+      config: qu
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 57
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (sw)
+      type: xcopa
+      config: sw
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 54
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (ta)
+      type: xcopa
+      config: ta
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 62
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (th)
+      type: xcopa
+      config: th
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 57
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (tr)
+      type: xcopa
+      config: tr
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 57
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (vi)
+      type: xcopa
+      config: vi
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 63
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (zh)
+      type: xcopa
+      config: zh
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 58
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (ar)
+      type: Muennighoff/xstory_cloze
+      config: ar
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 56.59
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (es)
+      type: Muennighoff/xstory_cloze
+      config: es
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 55.72
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (eu)
+      type: Muennighoff/xstory_cloze
+      config: eu
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 52.61
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (hi)
+      type: Muennighoff/xstory_cloze
+      config: hi
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 52.15
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (id)
+      type: Muennighoff/xstory_cloze
+      config: id
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 54.67
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (my)
+      type: Muennighoff/xstory_cloze
+      config: my
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 51.69
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (ru)
+      type: Muennighoff/xstory_cloze
+      config: ru
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 53.74
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (sw)
+      type: Muennighoff/xstory_cloze
+      config: sw
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 55.53
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (te)
+      type: Muennighoff/xstory_cloze
+      config: te
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 57.18
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (zh)
+      type: Muennighoff/xstory_cloze
+      config: zh
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 59.5
+---
+
+# cstr/mt0-large-Q4_K_M-GGUF
+This model was converted to GGUF format from [`bigscience/mt0-large`](https://huggingface.co/bigscience/mt0-large) 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-large) 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 cstr/mt0-large-Q4_K_M-GGUF --hf-file mt0-large-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+
+### Server:
+```bash
+llama-server --hf-repo cstr/mt0-large-Q4_K_M-GGUF --hf-file mt0-large-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 cstr/mt0-large-Q4_K_M-GGUF --hf-file mt0-large-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+or 
+```
+./llama-server --hf-repo cstr/mt0-large-Q4_K_M-GGUF --hf-file mt0-large-q4_k_m.gguf -c 2048
+```