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---
datasets:
- ewof/koishi-instruct-metharme
exported_from: ewof/koishi-8x7b-qlora
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
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static quants of https://huggingface.co/ewof/koishi-8x7b-qlora
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/koishi-8x7b-qlora-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/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.Q2_K.gguf) | Q2_K | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.IQ3_XS.gguf) | IQ3_XS | 19.5 | |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.IQ3_S.gguf) | IQ3_S | 20.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.Q3_K_S.gguf) | Q3_K_S | 20.5 | |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.IQ3_M.gguf) | IQ3_M | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.Q3_K_M.gguf) | Q3_K_M | 22.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.Q3_K_L.gguf) | Q3_K_L | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.IQ4_XS.gguf) | IQ4_XS | 25.5 | |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.Q4_K_S.gguf) | Q4_K_S | 26.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.Q4_K_M.gguf) | Q4_K_M | 28.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.Q5_K_S.gguf) | Q5_K_S | 32.3 | |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.Q5_K_M.gguf) | Q5_K_M | 33.3 | |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.Q6_K.gguf) | Q6_K | 38.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/koishi-8x7b-qlora-GGUF/resolve/main/koishi-8x7b-qlora.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
## 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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