gguf TensorBlock GGUF text-generation ak ar as bm bn ca code en es eu fon fr gu hi id ig ki kn lg ln ml mr ne nso ny or pa pt rn rw sn st sw ta te tn ts tum tw ur vi wo xh yo zh zu dataset:bigscience/xP3 base_model:bigscience/bloomz-1b1 base_model:quantized:bigscience/bloomz-1b1 license:bigscience-bloom-rail-1.0 model-index endpoints_compatible region:us
---
base_model: bigscience/bloomz-1b1
datasets:
- bigscience/xP3
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
license: bigscience-bloom-rail-1.0
pipeline_tag: text-generation
tags:
- TensorBlock
- GGUF
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
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
model-index:
- name: bloomz-1b1
  results:
  - task:
      type: Coreference resolution
    dataset:
      name: Winogrande XL (xl)
      type: winogrande
      config: xl
      split: validation
      revision: a80f460359d1e9a67c006011c94de42a8759430c
    metrics:
    - type: Accuracy
      value: 52.33
  - task:
      type: Coreference resolution
    dataset:
      name: XWinograd (en)
      type: Muennighoff/xwinograd
      config: en
      split: test
      revision: 9dd5ea5505fad86b7bedad667955577815300cee
    metrics:
    - type: Accuracy
      value: 50.49
  - task:
      type: Coreference resolution
    dataset:
      name: XWinograd (fr)
      type: Muennighoff/xwinograd
      config: fr
      split: test
      revision: 9dd5ea5505fad86b7bedad667955577815300cee
    metrics:
    - type: Accuracy
      value: 59.04
  - task:
      type: Coreference resolution
    dataset:
      name: XWinograd (jp)
      type: Muennighoff/xwinograd
      config: jp
      split: test
      revision: 9dd5ea5505fad86b7bedad667955577815300cee
    metrics:
    - type: Accuracy
      value: 51.82
  - task:
      type: Coreference resolution
    dataset:
      name: XWinograd (pt)
      type: Muennighoff/xwinograd
      config: pt
      split: test
      revision: 9dd5ea5505fad86b7bedad667955577815300cee
    metrics:
    - type: Accuracy
      value: 54.75
  - task:
      type: Coreference resolution
    dataset:
      name: XWinograd (ru)
      type: Muennighoff/xwinograd
      config: ru
      split: test
      revision: 9dd5ea5505fad86b7bedad667955577815300cee
    metrics:
    - type: Accuracy
      value: 53.97
  - task:
      type: Coreference resolution
    dataset:
      name: XWinograd (zh)
      type: Muennighoff/xwinograd
      config: zh
      split: test
      revision: 9dd5ea5505fad86b7bedad667955577815300cee
    metrics:
    - type: Accuracy
      value: 55.16
  - 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: 33.5
  - task:
      type: Natural language inference
    dataset:
      name: ANLI (r3)
      type: anli
      config: r3
      split: validation
      revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
    metrics:
    - type: Accuracy
      value: 34.5
  - task:
      type: Natural language inference
    dataset:
      name: SuperGLUE (cb)
      type: super_glue
      config: cb
      split: validation
      revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
    metrics:
    - type: Accuracy
      value: 58.93
  - task:
      type: Natural language inference
    dataset:
      name: SuperGLUE (rte)
      type: super_glue
      config: rte
      split: validation
      revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
    metrics:
    - type: Accuracy
      value: 65.7
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (ar)
      type: xnli
      config: ar
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 46.59
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (bg)
      type: xnli
      config: bg
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 40.4
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (de)
      type: xnli
      config: de
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 40.12
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (el)
      type: xnli
      config: el
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 39.32
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (en)
      type: xnli
      config: en
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 47.11
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (es)
      type: xnli
      config: es
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 47.55
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (fr)
      type: xnli
      config: fr
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 48.51
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (hi)
      type: xnli
      config: hi
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 42.89
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (ru)
      type: xnli
      config: ru
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 42.81
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (sw)
      type: xnli
      config: sw
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 41.29
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (th)
      type: xnli
      config: th
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 42.93
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (tr)
      type: xnli
      config: tr
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 37.51
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (ur)
      type: xnli
      config: ur
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 41.37
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (vi)
      type: xnli
      config: vi
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 47.19
  - task:
      type: Natural language inference
    dataset:
      name: XNLI (zh)
      type: xnli
      config: zh
      split: validation
      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
    metrics:
    - type: Accuracy
      value: 47.63
  - task:
      type: Program synthesis
    dataset:
      name: HumanEval
      type: openai_humaneval
      config: None
      split: test
      revision: e8dc562f5de170c54b5481011dd9f4fa04845771
    metrics:
    - type: Pass@1
      value: 2.62
    - type: Pass@10
      value: 6.22
    - type: Pass@100
      value: 11.68
  - task:
      type: Sentence completion
    dataset:
      name: StoryCloze (2016)
      type: story_cloze
      config: '2016'
      split: validation
      revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
    metrics:
    - type: Accuracy
      value: 62.75
  - task:
      type: Sentence completion
    dataset:
      name: SuperGLUE (copa)
      type: super_glue
      config: copa
      split: validation
      revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
    metrics:
    - type: Accuracy
      value: 63.0
  - task:
      type: Sentence completion
    dataset:
      name: XCOPA (et)
      type: xcopa
      config: et
      split: validation
      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
    metrics:
    - type: Accuracy
      value: 55.0
  - task:
      type: Sentence completion
    dataset:
      name: XCOPA (ht)
      type: xcopa
      config: ht
      split: validation
      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
    metrics:
    - type: Accuracy
      value: 52.0
  - task:
      type: Sentence completion
    dataset:
      name: XCOPA (id)
      type: xcopa
      config: id
      split: validation
      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
    metrics:
    - type: Accuracy
      value: 60.0
  - task:
      type: Sentence completion
    dataset:
      name: XCOPA (it)
      type: xcopa
      config: it
      split: validation
      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
    metrics:
    - type: Accuracy
      value: 56.0
  - task:
      type: Sentence completion
    dataset:
      name: XCOPA (qu)
      type: xcopa
      config: qu
      split: validation
      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
    metrics:
    - type: Accuracy
      value: 56.0
  - task:
      type: Sentence completion
    dataset:
      name: XCOPA (sw)
      type: xcopa
      config: sw
      split: validation
      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
    metrics:
    - type: Accuracy
      value: 64.0
  - task:
      type: Sentence completion
    dataset:
      name: XCOPA (ta)
      type: xcopa
      config: ta
      split: validation
      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
    metrics:
    - type: Accuracy
      value: 57.0
  - task:
      type: Sentence completion
    dataset:
      name: XCOPA (th)
      type: xcopa
      config: th
      split: validation
      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
    metrics:
    - type: Accuracy
      value: 59.0
  - task:
      type: Sentence completion
    dataset:
      name: XCOPA (tr)
      type: xcopa
      config: tr
      split: validation
      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
    metrics:
    - type: Accuracy
      value: 55.0
  - task:
      type: Sentence completion
    dataset:
      name: XCOPA (vi)
      type: xcopa
      config: vi
      split: validation
      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
    metrics:
    - type: Accuracy
      value: 63.0
  - task:
      type: Sentence completion
    dataset:
      name: XCOPA (zh)
      type: xcopa
      config: zh
      split: validation
      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
    metrics:
    - type: Accuracy
      value: 61.0
  - task:
      type: Sentence completion
    dataset:
      name: XStoryCloze (ar)
      type: Muennighoff/xstory_cloze
      config: ar
      split: validation
      revision: 8bb76e594b68147f1a430e86829d07189622b90d
    metrics:
    - type: Accuracy
      value: 53.54
  - task:
      type: Sentence completion
    dataset:
      name: XStoryCloze (es)
      type: Muennighoff/xstory_cloze
      config: es
      split: validation
      revision: 8bb76e594b68147f1a430e86829d07189622b90d
    metrics:
    - type: Accuracy
      value: 58.37
  - task:
      type: Sentence completion
    dataset:
      name: XStoryCloze (eu)
      type: Muennighoff/xstory_cloze
      config: eu
      split: validation
      revision: 8bb76e594b68147f1a430e86829d07189622b90d
    metrics:
    - type: Accuracy
      value: 52.35
  - task:
      type: Sentence completion
    dataset:
      name: XStoryCloze (hi)
      type: Muennighoff/xstory_cloze
      config: hi
      split: validation
      revision: 8bb76e594b68147f1a430e86829d07189622b90d
    metrics:
    - type: Accuracy
      value: 55.92
  - task:
      type: Sentence completion
    dataset:
      name: XStoryCloze (id)
      type: Muennighoff/xstory_cloze
      config: id
      split: validation
      revision: 8bb76e594b68147f1a430e86829d07189622b90d
    metrics:
    - type: Accuracy
      value: 57.97
  - task:
      type: Sentence completion
    dataset:
      name: XStoryCloze (my)
      type: Muennighoff/xstory_cloze
      config: my
      split: validation
      revision: 8bb76e594b68147f1a430e86829d07189622b90d
    metrics:
    - type: Accuracy
      value: 47.05
  - task:
      type: Sentence completion
    dataset:
      name: XStoryCloze (ru)
      type: Muennighoff/xstory_cloze
      config: ru
      split: validation
      revision: 8bb76e594b68147f1a430e86829d07189622b90d
    metrics:
    - type: Accuracy
      value: 50.3
  - task:
      type: Sentence completion
    dataset:
      name: XStoryCloze (sw)
      type: Muennighoff/xstory_cloze
      config: sw
      split: validation
      revision: 8bb76e594b68147f1a430e86829d07189622b90d
    metrics:
    - type: Accuracy
      value: 49.97
  - task:
      type: Sentence completion
    dataset:
      name: XStoryCloze (te)
      type: Muennighoff/xstory_cloze
      config: te
      split: validation
      revision: 8bb76e594b68147f1a430e86829d07189622b90d
    metrics:
    - type: Accuracy
      value: 55.86
  - task:
      type: Sentence completion
    dataset:
      name: XStoryCloze (zh)
      type: Muennighoff/xstory_cloze
      config: zh
      split: validation
      revision: 8bb76e594b68147f1a430e86829d07189622b90d
    metrics:
    - type: Accuracy
      value: 58.17
---

<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;">
            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
        </p>
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</div>

## bigscience/bloomz-1b1 - GGUF

This repo contains GGUF format model files for [bigscience/bloomz-1b1](https://huggingface.co/bigscience/bloomz-1b1).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).


<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template


```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [bloomz-1b1-Q2_K.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q2_K.gguf) | Q2_K | 0.660 GB | smallest, significant quality loss - not recommended for most purposes |
| [bloomz-1b1-Q3_K_S.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q3_K_S.gguf) | Q3_K_S | 0.734 GB | very small, high quality loss |
| [bloomz-1b1-Q3_K_M.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q3_K_M.gguf) | Q3_K_M | 0.791 GB | very small, high quality loss |
| [bloomz-1b1-Q3_K_L.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q3_K_L.gguf) | Q3_K_L | 0.823 GB | small, substantial quality loss |
| [bloomz-1b1-Q4_0.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q4_0.gguf) | Q4_0 | 0.866 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [bloomz-1b1-Q4_K_S.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q4_K_S.gguf) | Q4_K_S | 0.869 GB | small, greater quality loss |
| [bloomz-1b1-Q4_K_M.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q4_K_M.gguf) | Q4_K_M | 0.913 GB | medium, balanced quality - recommended |
| [bloomz-1b1-Q5_0.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q5_0.gguf) | Q5_0 | 0.990 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [bloomz-1b1-Q5_K_S.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q5_K_S.gguf) | Q5_K_S | 0.990 GB | large, low quality loss - recommended |
| [bloomz-1b1-Q5_K_M.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q5_K_M.gguf) | Q5_K_M | 1.025 GB | large, very low quality loss - recommended |
| [bloomz-1b1-Q6_K.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q6_K.gguf) | Q6_K | 1.121 GB | very large, extremely low quality loss |
| [bloomz-1b1-Q8_0.gguf](https://huggingface.co/tensorblock/bloomz-1b1-GGUF/blob/main/bloomz-1b1-Q8_0.gguf) | Q8_0 | 1.449 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/bloomz-1b1-GGUF --include "bloomz-1b1-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/bloomz-1b1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
