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初始化项目，由ModelHub XC社区提供模型 Model: ysn-rfd/bloomz-3b-GGUF Source: Original Platform
2026-04-22 20:52:04 +08:00
---
base_model: bigscience/bloomz-3b
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:
- llama-cpp
- matrixportal
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-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: 40.0
- task:
type: Natural language inference
dataset:
name: SuperGLUE (cb)
type: super_glue
config: cb
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 75.0
- 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: 76.0
- task:
type: Sentence completion
dataset:
name: XCOPA (et)
type: xcopa
config: et
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 53.0
- task:
type: Sentence completion
dataset:
name: XCOPA (ht)
type: xcopa
config: ht
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 64.0
- task:
type: Sentence completion
dataset:
name: XCOPA (id)
type: xcopa
config: id
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 70.0
- task:
type: Sentence completion
dataset:
name: XCOPA (it)
type: xcopa
config: it
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 53.0
- 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: 66.0
- task:
type: Sentence completion
dataset:
name: XCOPA (ta)
type: xcopa
config: ta
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 59.0
- task:
type: Sentence completion
dataset:
name: XCOPA (th)
type: xcopa
config: th
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 63.0
- task:
type: Sentence completion
dataset:
name: XCOPA (tr)
type: xcopa
config: tr
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 61.0
- task:
type: Sentence completion
dataset:
name: XCOPA (vi)
type: xcopa
config: vi
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 77.0
- task:
type: Sentence completion
dataset:
name: XCOPA (zh)
type: xcopa
config: zh
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 73.0
- 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
---
# ysn-rfd/bloomz-3b-GGUF
This model was converted to GGUF format from [`bigscience/bloomz-3b`](https://huggingface.co/bigscience/bloomz-3b) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
Refer to the [original model card](https://huggingface.co/bigscience/bloomz-3b) for more details on the model.
## ✅ Quantized Models Download List
### 🔍 Recommended Quantizations
- **✨ General CPU Use:** [`Q4_K_M`](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q4_k_m.gguf) (Best balance of speed/quality)
- **📱 ARM Devices:** [`Q4_0`](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q4_0.gguf) (Optimized for ARM CPUs)
- **🏆 Maximum Quality:** [`Q8_0`](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q8_0.gguf) (Near-original quality)
### 📦 Full Quantization Options
| 🚀 Download | 🔢 Type | 📝 Notes |
|:---------|:-----|:------|
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q2_k.gguf) | ![Q2_K](https://img.shields.io/badge/Q2_K-1A73E8) | Basic quantization |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q3_k_s.gguf) | ![Q3_K_S](https://img.shields.io/badge/Q3_K_S-34A853) | Small size |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q3_k_m.gguf) | ![Q3_K_M](https://img.shields.io/badge/Q3_K_M-FBBC05) | Balanced quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q3_k_l.gguf) | ![Q3_K_L](https://img.shields.io/badge/Q3_K_L-4285F4) | Better quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q4_0.gguf) | ![Q4_0](https://img.shields.io/badge/Q4_0-EA4335) | Fast on ARM |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q4_k_s.gguf) | ![Q4_K_S](https://img.shields.io/badge/Q4_K_S-673AB7) | Fast, recommended |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q4_k_m.gguf) | ![Q4_K_M](https://img.shields.io/badge/Q4_K_M-673AB7) ⭐ | Best balance |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q5_0.gguf) | ![Q5_0](https://img.shields.io/badge/Q5_0-FF6D01) | Good quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q5_k_s.gguf) | ![Q5_K_S](https://img.shields.io/badge/Q5_K_S-0F9D58) | Balanced |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q5_k_m.gguf) | ![Q5_K_M](https://img.shields.io/badge/Q5_K_M-0F9D58) | High quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q6_k.gguf) | ![Q6_K](https://img.shields.io/badge/Q6_K-4285F4) 🏆 | Very good quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q8_0.gguf) | ![Q8_0](https://img.shields.io/badge/Q8_0-EA4335) ⚡ | Fast, best quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-f16.gguf) | ![F16](https://img.shields.io/badge/F16-000000) | Maximum accuracy |
💡 **Tip:** Use `F16` for maximum precision when quality is critical
---
# 🚀 Applications and Tools for Locally Quantized LLMs
## 🖥️ Desktop Applications
| Application | Description | Download Link |
|-----------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **Llama.cpp** | A fast and efficient inference engine for GGUF models. | [GitHub Repository](https://github.com/ggml-org/llama.cpp) |
| **Ollama** | A streamlined solution for running LLMs locally. | [Website](https://ollama.com/) |
| **AnythingLLM** | An AI-powered knowledge management tool. | [GitHub Repository](https://github.com/Mintplex-Labs/anything-llm) |
| **Open WebUI** | A user-friendly web interface for running local LLMs. | [GitHub Repository](https://github.com/open-webui/open-webui) |
| **GPT4All** | A user-friendly desktop application supporting various LLMs, compatible with GGUF models. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
| **LM Studio** | A desktop application designed to run and manage local LLMs, supporting GGUF format. | [Website](https://lmstudio.ai/) |
| **GPT4All Chat**| A chat application compatible with GGUF models for local, offline interactions. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
---
## 📱 Mobile Applications
| Application | Description | Download Link |
|-------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **ChatterUI** | A simple and lightweight LLM app for mobile devices. | [GitHub Repository](https://github.com/Vali-98/ChatterUI) |
| **Maid** | Mobile Artificial Intelligence Distribution for running AI models on mobile devices. | [GitHub Repository](https://github.com/Mobile-Artificial-Intelligence/maid) |
| **PocketPal AI** | A mobile AI assistant powered by local models. | [GitHub Repository](https://github.com/a-ghorbani/pocketpal-ai) |
| **Layla** | A flexible platform for running various AI models on mobile devices. | [Website](https://www.layla-network.ai/) |
---
## 🎨 Image Generation Applications
| Application | Description | Download Link |
|-------------------------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **Stable Diffusion** | An open-source AI model for generating images from text. | [GitHub Repository](https://github.com/CompVis/stable-diffusion) |
| **Stable Diffusion WebUI** | A web application providing access to Stable Diffusion models via a browser interface. | [GitHub Repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) |
| **Local Dream** | Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference. | [GitHub Repository](https://github.com/xororz/local-dream) |
| **Stable-Diffusion-Android (SDAI)** | An open-source AI art application for Android devices, enabling digital art creation. | [GitHub Repository](https://github.com/ShiftHackZ/Stable-Diffusion-Android) |
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