metadata
datasets:
- bigscience/xP3
license: bigscience-bloom-rail-1.0
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
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
pipeline_tag: text-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:
- TensorBlock
- GGUF
base_model: bigscience/bloomz-3b
model-index:
- name: bloomz-3b1
results:
- task:
type: Coreference resolution
dataset:
name: Winogrande XL (xl)
type: winogrande
config: xl
split: validation
revision: a80f460359d1e9a67c006011c94de42a8759430c
metrics:
- type: Accuracy
value: 53.67
- task:
type: Coreference resolution
dataset:
name: XWinograd (en)
type: Muennighoff/xwinograd
config: en
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 59.23
- task:
type: Coreference resolution
dataset:
name: XWinograd (fr)
type: Muennighoff/xwinograd
config: fr
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 53.01
- task:
type: Coreference resolution
dataset:
name: XWinograd (jp)
type: Muennighoff/xwinograd
config: jp
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 52.45
- task:
type: Coreference resolution
dataset:
name: XWinograd (pt)
type: Muennighoff/xwinograd
config: pt
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 53.61
- 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: 60.91
- task:
type: Natural language inference
dataset:
name: ANLI (r1)
type: anli
config: r1
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 40.1
- task:
type: Natural language inference
dataset:
name: ANLI (r2)
type: anli
config: r2
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 36.8
- task:
type: Natural language inference
dataset:
name: ANLI (r3)
type: anli
config: r3
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 40
- task:
type: Natural language inference
dataset:
name: SuperGLUE (cb)
type: super_glue
config: cb
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 75
- task:
type: Natural language inference
dataset:
name: SuperGLUE (rte)
type: super_glue
config: rte
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 76.17
- task:
type: Natural language inference
dataset:
name: XNLI (ar)
type: xnli
config: ar
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 53.29
- task:
type: Natural language inference
dataset:
name: XNLI (bg)
type: xnli
config: bg
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 43.82
- task:
type: Natural language inference
dataset:
name: XNLI (de)
type: xnli
config: de
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 45.26
- task:
type: Natural language inference
dataset:
name: XNLI (el)
type: xnli
config: el
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 42.61
- task:
type: Natural language inference
dataset:
name: XNLI (en)
type: xnli
config: en
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 57.31
- task:
type: Natural language inference
dataset:
name: XNLI (es)
type: xnli
config: es
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 56.14
- task:
type: Natural language inference
dataset:
name: XNLI (fr)
type: xnli
config: fr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 55.78
- task:
type: Natural language inference
dataset:
name: XNLI (hi)
type: xnli
config: hi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 51.49
- task:
type: Natural language inference
dataset:
name: XNLI (ru)
type: xnli
config: ru
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 47.11
- task:
type: Natural language inference
dataset:
name: XNLI (sw)
type: xnli
config: sw
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 47.83
- 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: 37.23
- task:
type: Natural language inference
dataset:
name: XNLI (ur)
type: xnli
config: ur
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 49.04
- task:
type: Natural language inference
dataset:
name: XNLI (vi)
type: xnli
config: vi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 53.98
- task:
type: Natural language inference
dataset:
name: XNLI (zh)
type: xnli
config: zh
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 54.18
- task:
type: Program synthesis
dataset:
name: HumanEval
type: openai_humaneval
config: None
split: test
revision: e8dc562f5de170c54b5481011dd9f4fa04845771
metrics:
- type: Pass@1
value: 6.29
- type: Pass@10
value: 11.94
- type: Pass@100
value: 19.06
- task:
type: Sentence completion
dataset:
name: StoryCloze (2016)
type: story_cloze
config: '2016'
split: validation
revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
metrics:
- type: Accuracy
value: 87.33
- task:
type: Sentence completion
dataset:
name: SuperGLUE (copa)
type: super_glue
config: copa
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 76
- task:
type: Sentence completion
dataset:
name: XCOPA (et)
type: xcopa
config: et
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 53
- task:
type: Sentence completion
dataset:
name: XCOPA (ht)
type: xcopa
config: ht
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 64
- task:
type: Sentence completion
dataset:
name: XCOPA (id)
type: xcopa
config: id
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 70
- task:
type: Sentence completion
dataset:
name: XCOPA (it)
type: xcopa
config: it
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 53
- 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: 66
- task:
type: Sentence completion
dataset:
name: XCOPA (ta)
type: xcopa
config: ta
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 59
- task:
type: Sentence completion
dataset:
name: XCOPA (th)
type: xcopa
config: th
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 63
- task:
type: Sentence completion
dataset:
name: XCOPA (tr)
type: xcopa
config: tr
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 61
- task:
type: Sentence completion
dataset:
name: XCOPA (vi)
type: xcopa
config: vi
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 77
- task:
type: Sentence completion
dataset:
name: XCOPA (zh)
type: xcopa
config: zh
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 73
- task:
type: Sentence completion
dataset:
name: XStoryCloze (ar)
type: Muennighoff/xstory_cloze
config: ar
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 80.61
- task:
type: Sentence completion
dataset:
name: XStoryCloze (es)
type: Muennighoff/xstory_cloze
config: es
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 85.9
- task:
type: Sentence completion
dataset:
name: XStoryCloze (eu)
type: Muennighoff/xstory_cloze
config: eu
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 70.95
- task:
type: Sentence completion
dataset:
name: XStoryCloze (hi)
type: Muennighoff/xstory_cloze
config: hi
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 78.89
- task:
type: Sentence completion
dataset:
name: XStoryCloze (id)
type: Muennighoff/xstory_cloze
config: id
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 82.99
- task:
type: Sentence completion
dataset:
name: XStoryCloze (my)
type: Muennighoff/xstory_cloze
config: my
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 49.9
- task:
type: Sentence completion
dataset:
name: XStoryCloze (ru)
type: Muennighoff/xstory_cloze
config: ru
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 61.42
- task:
type: Sentence completion
dataset:
name: XStoryCloze (sw)
type: Muennighoff/xstory_cloze
config: sw
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 69.69
- task:
type: Sentence completion
dataset:
name: XStoryCloze (te)
type: Muennighoff/xstory_cloze
config: te
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 73.66
- task:
type: Sentence completion
dataset:
name: XStoryCloze (zh)
type: Muennighoff/xstory_cloze
config: zh
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 84.32
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
bigscience/bloomz-3b - GGUF
This repo contains GGUF format model files for bigscience/bloomz-3b.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Our projects
| Awesome MCP Servers | TensorBlock Studio |
|---|---|
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| A comprehensive collection of Model Context Protocol (MCP) servers. | A lightweight, open, and extensible multi-LLM interaction studio. |
| 👀 See what we built 👀 | 👀 See what we built 👀 |
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| bloomz-3b-Q2_K.gguf | Q2_K | 1.516 GB | smallest, significant quality loss - not recommended for most purposes |
| bloomz-3b-Q3_K_S.gguf | Q3_K_S | 1.707 GB | very small, high quality loss |
| bloomz-3b-Q3_K_M.gguf | Q3_K_M | 1.905 GB | very small, high quality loss |
| bloomz-3b-Q3_K_L.gguf | Q3_K_L | 2.016 GB | small, substantial quality loss |
| bloomz-3b-Q4_0.gguf | Q4_0 | 2.079 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| bloomz-3b-Q4_K_S.gguf | Q4_K_S | 2.088 GB | small, greater quality loss |
| bloomz-3b-Q4_K_M.gguf | Q4_K_M | 2.235 GB | medium, balanced quality - recommended |
| bloomz-3b-Q5_0.gguf | Q5_0 | 2.428 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| bloomz-3b-Q5_K_S.gguf | Q5_K_S | 2.428 GB | large, low quality loss - recommended |
| bloomz-3b-Q5_K_M.gguf | Q5_K_M | 2.546 GB | large, very low quality loss - recommended |
| bloomz-3b-Q6_K.gguf | Q6_K | 2.799 GB | very large, extremely low quality loss |
| bloomz-3b-Q8_0.gguf | Q8_0 | 3.621 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/bloomz-3b-GGUF --include "bloomz-3b-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:
huggingface-cli download tensorblock/bloomz-3b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'