---
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
```