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---
base_model: wenbopan/Faro-Yi-34B
datasets:
- wenbopan/Fusang-v1
- wenbopan/OpenOrca-zh-20k
language:
- zh
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
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static quants of https://huggingface.co/wenbopan/Faro-Yi-34B
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Faro-Yi-34B-200K-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/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.Q2_K.gguf) | Q2_K | 13.5 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.IQ3_XS.gguf) | IQ3_XS | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.Q3_K_S.gguf) | Q3_K_S | 15.6 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.IQ3_S.gguf) | IQ3_S | 15.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.IQ3_M.gguf) | IQ3_M | 16.2 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.Q3_K_M.gguf) | Q3_K_M | 17.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.Q3_K_L.gguf) | Q3_K_L | 18.8 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.IQ4_XS.gguf) | IQ4_XS | 19.3 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.Q4_K_S.gguf) | Q4_K_S | 20.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.Q4_K_M.gguf) | Q4_K_M | 21.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.Q5_K_S.gguf) | Q5_K_S | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.Q5_K_M.gguf) | Q5_K_M | 25.0 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.Q6_K.gguf) | Q6_K | 28.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-200K-GGUF/resolve/main/Faro-Yi-34B-200K.Q8_0.gguf) | Q8_0 | 37.1 | 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
## 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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