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---
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
language:
- fr
- it
- de
- es
- 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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static quants are available at https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-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://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 9.8 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 10.8 | mostly desperate |
| [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 | 12.6 | |
| [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 | 13.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 14.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 15.8 | |
| [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 | 17.6 | 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 | 18.6 | 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 | 19.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 19.5 | |
| [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 | 20.7 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ3_S.gguf) | i1-IQ3_S | 20.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ3_M.gguf) | i1-IQ3_M | 21.7 | |
| [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 | 22.8 | 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 | 24.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 25.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-IQ4_NL.gguf) | i1-IQ4_NL | 26.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-Q4_0.gguf) | i1-Q4_0 | 26.8 | fast, low quality |
| [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 | 27.0 | optimal size/speed/quality |
| [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 | 28.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 32.5 | |
| [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 | 33.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-i1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1.i1-Q6_K.gguf) | i1-Q6_K | 38.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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.
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