---
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About

weighted/imatrix quants of https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1

Artefact2 also made some, with a different weight matrix, at https://huggingface.co/Artefact2/Mixtral-8x7B-Instruct-v0.1-GGUF


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## 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://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 9.7 |  |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 12.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 13.8 |  |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-Q2_K.gguf) | i1-Q2_K | 17.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 18.5 | fast, lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-Q3_K_XS.gguf) | i1-Q3_K_XS | 19.2 |  |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 20.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 22.7 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 24.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 26.9 | almost as good as Q4_K_M |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 28.6 | fast, medium quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 33.4 | best weighted quant |


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

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