---
datasets:
- wenbopan/Fusang-v1
- wenbopan/OpenOrca-zh-20k
exported_from: wenbopan/Faro-Yi-34B-200K
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About

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weighted/imatrix quants of https://huggingface.co/wenbopan/Faro-Yi-34B-200K

**This uses my "quarter" training set of 40k tokens as the model overflowed after 25k tokens with the standard set.**

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static quants are available at https://huggingface.co/mradermacher/Faro-Yi-34B-200K-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/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-Q2_K.gguf) | i1-Q2_K | 13.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 14.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-Q3_K_M.gguf) | i1-Q3_K_M | 17.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-Q4_0.gguf) | i1-Q4_0 | 20.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-Q4_K_S.gguf) | i1-Q4_K_S | 20.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-Q4_K_M.gguf) | i1-Q4_K_M | 21.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-Q5_K_S.gguf) | i1-Q5_K_S | 24.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-Q5_K_M.gguf) | i1-Q5_K_M | 25.0 |  |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-Q6_K.gguf) | i1-Q6_K | 28.9 | practically like static Q6_K |


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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