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---
base_model: vicky4s4s/Mixtral-instruct-56B
datasets:
- HuggingFaceH4/ultrachat_200k
- liuhaotian/LLaVA-Instruct-150K
language:
- fr
- it
- de
- es
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
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weighted/imatrix quants of https://huggingface.co/vicky4s4s/Mixtral-instruct-56B
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static quants are available at https://huggingface.co/mradermacher/Mixtral-instruct-56B-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-instruct-56B-i1-GGUF/resolve/main/Mixtral-instruct-56B.i1-Q2_K.gguf) | i1-Q2_K | 17.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-instruct-56B-i1-GGUF/resolve/main/Mixtral-instruct-56B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 26.8 | optimal size/speed/quality |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
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