---
title: Llama-3.2-1B-Instruct-abliterated3-i1-GGUF
canonical_url: "https://www.modelscope.cn/models/mradermacher/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF"
md_url: "https://www.modelscope.cn/models/mradermacher/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF.md"
repository: mradermacher/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF
last_updated: 2025-08-10
license: llama3.2
base_model:
  - mylesgoose/Llama-3.2-1B-Instruct-abliterated3
base_model_relation: quantized
library_name:
  - gguf
language:
  - en
  - de
  - fr
  - it
  - pt
  - hi
  - es
  - th
downloads: 100
stars: 0
tags:
  - facebook
  - meta
  - pytorch
  - llama
  - llama-3
  - gguf
---

# Llama-3.2-1B-Instruct-abliterated3-i1-GGUF

> Llama-3.2-1B-Instruct-abliterated3-i1-GGUF - mradermacher 在 ModelScope 开源的模型。weighted/imatrix quants of https://huggingface.co/mylesgoose/Llama-3.2-1B-Instruct-abliterated3

mradermacher/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF 是 ModelScope 魔搭社区上的机器学习模型，采用 llama3.2 许可，基于 mylesgoose/Llama-3.2-1B-Instruct-abliterated3 构建。

- **Repository**: mradermacher/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF
- **License**: llama3.2
- **Base model**: mylesgoose/Llama-3.2-1B-Instruct-abliterated3
- **Tags**: facebook, meta, pytorch, llama, llama-3, gguf
- **Downloads**: 100
- **Stars**: 0
- **Last updated**: 2025-08-10

Source: https://www.modelscope.cn/models/mradermacher/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF

---

## About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/mylesgoose/Llama-3.2-1B-Instruct-abliterated3

<!-- provided-files -->

***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Llama-3.2-1B-Instruct-abliterated3-i1-GGUF).***

static quants are available at https://huggingface.co/mradermacher/Llama-3.2-1B-Instruct-abliterated3-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/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF/resolve/master/Llama-3.2-1B-Instruct-abliterated3.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.6 |  |
| [GGUF](https://www.modelscope.cn/models/mradermacher/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF/resolve/master/Llama-3.2-1B-Instruct-abliterated3.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.7 |  |
| [GGUF](https://www.modelscope.cn/models/mradermacher/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF/resolve/master/Llama-3.2-1B-Instruct-abliterated3.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.8 |  |
| [GGUF](https://www.modelscope.cn/models/mradermacher/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF/resolve/master/Llama-3.2-1B-Instruct-abliterated3.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.9 | fast, recommended |
| [GGUF](https://www.modelscope.cn/models/mradermacher/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF/resolve/master/Llama-3.2-1B-Instruct-abliterated3.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.0 |  |
| [GGUF](https://www.modelscope.cn/models/mradermacher/Llama-3.2-1B-Instruct-abliterated3-i1-GGUF/resolve/master/Llama-3.2-1B-Instruct-abliterated3.i1-Q6_K.gguf) | i1-Q6_K | 1.1 | 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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