---
title: Huihui-Qwen3-8B-abliterated-v2-i1-GGUF
canonical_url: "https://www.modelscope.cn/models/mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF"
md_url: "https://www.modelscope.cn/models/mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF.md"
repository: mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF
last_updated: 2025-07-29
license: apache-2.0
base_model:
  - huihui-ai/Huihui-Qwen3-8B-abliterated-v2
base_model_relation: quantized
library_name:
  - gguf
language:
  - en
downloads: 3808
stars: 8
tags:
  - chat
  - abliterated
  - uncensored
  - gguf
---

# Huihui-Qwen3-8B-abliterated-v2-i1-GGUF

> Huihui-Qwen3-8B-abliterated-v2-i1-GGUF - mradermacher 在 ModelScope 开源的模型。weighted/imatrix quants of https://huggingface.co/huihui-ai/Huihui-Qwen3-8B-abliterated-v2

mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可，基于 huihui-ai/Huihui-Qwen3-8B-abliterated-v2 构建。

- **Repository**: mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF
- **License**: apache-2.0
- **Base model**: huihui-ai/Huihui-Qwen3-8B-abliterated-v2
- **Tags**: chat, abliterated, uncensored, gguf
- **Downloads**: 3808
- **Stars**: 8
- **Last updated**: 2025-07-29

Source: https://www.modelscope.cn/models/mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF

---

## About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/huihui-ai/Huihui-Qwen3-8B-abliterated-v2

<!-- provided-files -->

***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Huihui-Qwen3-8B-abliterated-v2-i1-GGUF).***

static quants are available at https://huggingface.co/mradermacher/Huihui-Qwen3-8B-abliterated-v2-GGUF
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.

## Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://www.modelscope.cn/models/mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF/resolve/master/Huihui-Qwen3-8B-abliterated-v2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.8 |  |
| [GGUF](https://www.modelscope.cn/models/mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF/resolve/master/Huihui-Qwen3-8B-abliterated-v2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.7 |  |
| [GGUF](https://www.modelscope.cn/models/mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF/resolve/master/Huihui-Qwen3-8B-abliterated-v2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.7 |  |
| [GGUF](https://www.modelscope.cn/models/mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF/resolve/master/Huihui-Qwen3-8B-abliterated-v2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://www.modelscope.cn/models/mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF/resolve/master/Huihui-Qwen3-8B-abliterated-v2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.0 |  |
| [GGUF](https://www.modelscope.cn/models/mradermacher/Huihui-Qwen3-8B-abliterated-v2-i1-GGUF/resolve/master/Huihui-Qwen3-8B-abliterated-v2.i1-Q6_K.gguf) | i1-Q6_K | 6.8 | practically like static Q6_K |

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

## FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.

## Thanks

I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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