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---
datasets:
- wenbopan/Chinese-dpo-pairs
- Intel/orca_dpo_pairs
- argilla/ultrafeedback-binarized-preferences-cleaned
- jondurbin/truthy-dpo-v0.1
exported_from: wenbopan/Faro-Yi-34B-DPO
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-DPO
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static quants are available at https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-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-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/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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