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---
exported_from: cloudyu/Mixtral_34Bx2_MoE_60B
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
license_name: yi-license
quantized_by: mradermacher
tags:
- yi
- moe
---
## About
static quants of https://huggingface.co/cloudyu/Mixtral_34Bx2_MoE_60B
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weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q2_K.gguf) | Q2_K | 23.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.IQ3_XS.gguf) | IQ3_XS | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q3_K_S.gguf) | Q3_K_S | 27.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.IQ3_S.gguf) | IQ3_S | 27.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.IQ3_M.gguf) | IQ3_M | 27.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q3_K_M.gguf) | Q3_K_M | 29.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q3_K_L.gguf) | Q3_K_L | 32.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.IQ4_XS.gguf) | IQ4_XS | 33.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q4_0.gguf) | Q4_0 | 35.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q4_K_S.gguf) | Q4_K_S | 35.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.IQ4_NL.gguf) | IQ4_NL | 35.3 | slightly worse than Q4_K_S |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q4_K_M.gguf) | Q4_K_M | 37.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q5_K_S.gguf) | Q5_K_S | 42.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q5_K_M.gguf) | Q5_K_M | 43.7 | |
| [PART 1](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q6_K.gguf.part2of2) | Q6_K | 50.5 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral_34Bx2_MoE_60B-GGUF/resolve/main/Mixtral_34Bx2_MoE_60B.Q8_0.gguf.part2of2) | Q8_0 | 65.1 | 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
## 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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