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---
base_model: Envoid/Mixtral-Instruct-ITR-DADA-8x7B
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
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static quants of https://huggingface.co/Envoid/Mixtral-Instruct-ITR-DADA-8x7B
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mixtral-Instruct-ITR-DADA-8x7B-i1-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-ITR-DADA-8x7B-GGUF/resolve/main/Mixtral-Instruct-ITR-DADA-8x7B.Q2_K.gguf) | Q2_K | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-Instruct-ITR-DADA-8x7B-GGUF/resolve/main/Mixtral-Instruct-ITR-DADA-8x7B.Q3_K_S.gguf) | Q3_K_S | 20.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-Instruct-ITR-DADA-8x7B-GGUF/resolve/main/Mixtral-Instruct-ITR-DADA-8x7B.Q3_K_M.gguf) | Q3_K_M | 22.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-Instruct-ITR-DADA-8x7B-GGUF/resolve/main/Mixtral-Instruct-ITR-DADA-8x7B.Q3_K_L.gguf) | Q3_K_L | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-Instruct-ITR-DADA-8x7B-GGUF/resolve/main/Mixtral-Instruct-ITR-DADA-8x7B.IQ4_XS.gguf) | IQ4_XS | 25.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-Instruct-ITR-DADA-8x7B-GGUF/resolve/main/Mixtral-Instruct-ITR-DADA-8x7B.Q4_K_S.gguf) | Q4_K_S | 26.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-Instruct-ITR-DADA-8x7B-GGUF/resolve/main/Mixtral-Instruct-ITR-DADA-8x7B.Q4_K_M.gguf) | Q4_K_M | 28.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-Instruct-ITR-DADA-8x7B-GGUF/resolve/main/Mixtral-Instruct-ITR-DADA-8x7B.Q5_K_S.gguf) | Q5_K_S | 32.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-Instruct-ITR-DADA-8x7B-GGUF/resolve/main/Mixtral-Instruct-ITR-DADA-8x7B.Q5_K_M.gguf) | Q5_K_M | 33.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-Instruct-ITR-DADA-8x7B-GGUF/resolve/main/Mixtral-Instruct-ITR-DADA-8x7B.Q6_K.gguf) | Q6_K | 38.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-Instruct-ITR-DADA-8x7B-GGUF/resolve/main/Mixtral-Instruct-ITR-DADA-8x7B.Q8_0.gguf) | Q8_0 | 49.7 | fast, best 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.
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