---
base_model: MaziyarPanahi/Qwen1.5-8x7b-v0.1
datasets:
- Crystalcareai/MoD-150k
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE
license_name: tongyi-qianwen
quantized_by: mradermacher
tags:
- axolotl
- generated_from_trainer
- moe
- qwen
- mixtral
- text-generation-inference
---
## About

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static quants of https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b-v0.1

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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-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/Qwen1.5-8x7b-v0.1-GGUF/resolve/main/Qwen1.5-8x7b-v0.1.Q2_K.gguf) | Q2_K | 15.0 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-GGUF/resolve/main/Qwen1.5-8x7b-v0.1.Q3_K_S.gguf) | Q3_K_S | 17.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-GGUF/resolve/main/Qwen1.5-8x7b-v0.1.Q3_K_M.gguf) | Q3_K_M | 19.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-GGUF/resolve/main/Qwen1.5-8x7b-v0.1.Q3_K_L.gguf) | Q3_K_L | 20.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-GGUF/resolve/main/Qwen1.5-8x7b-v0.1.IQ4_XS.gguf) | IQ4_XS | 21.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-GGUF/resolve/main/Qwen1.5-8x7b-v0.1.Q4_K_S.gguf) | Q4_K_S | 22.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-GGUF/resolve/main/Qwen1.5-8x7b-v0.1.Q4_K_M.gguf) | Q4_K_M | 23.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-GGUF/resolve/main/Qwen1.5-8x7b-v0.1.Q5_K_S.gguf) | Q5_K_S | 26.7 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-GGUF/resolve/main/Qwen1.5-8x7b-v0.1.Q5_K_M.gguf) | Q5_K_M | 27.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-GGUF/resolve/main/Qwen1.5-8x7b-v0.1.Q6_K.gguf) | Q6_K | 31.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-GGUF/resolve/main/Qwen1.5-8x7b-v0.1.Q8_0.gguf) | Q8_0 | 40.5 | fast, best quality |

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

## 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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