---
exported_from: Undi95/Miqu-70B-Alpaca-DPO
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About

static quants of https://huggingface.co/Undi95/Miqu-70B-Alpaca-DPO

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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/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q2_K.gguf) | Q2_K | 25.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.IQ3_XS.gguf) | IQ3_XS | 28.7 |  |
| [GGUF](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q3_K_S.gguf) | Q3_K_S | 30.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q3_K_M.gguf) | Q3_K_M | 33.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q3_K_L.gguf) | Q3_K_L | 36.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q4_0.gguf) | Q4_0 | 39.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q4_K_S.gguf) | Q4_K_S | 39.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q4_K_M.gguf) | Q4_K_M | 41.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q5_K_S.gguf) | Q5_K_S | 47.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q5_K_M.gguf) | Q5_K_M | 49.2 |  |
| [PART 1](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q6_K.gguf.part2of2) | Q6_K | 57.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Miqu-70B-Alpaca-DPO-GGUF/resolve/main/Miqu-70B-Alpaca-DPO.Q8_0.gguf.part2of2) | Q8_0 | 73.6 | 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

## 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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