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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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static quants of https://huggingface.co/wenbopan/Faro-Yi-34B-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/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.Q2_K.gguf) | Q2_K | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.Q3_K_S.gguf) | Q3_K_S | 15.1 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.IQ3_S.gguf) | IQ3_S | 15.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.IQ3_M.gguf) | IQ3_M | 15.7 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.Q3_K_M.gguf) | Q3_K_M | 16.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.Q3_K_L.gguf) | Q3_K_L | 18.2 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.Q4_K_S.gguf) | Q4_K_S | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.Q4_K_M.gguf) | Q4_K_M | 20.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.Q5_K_S.gguf) | Q5_K_S | 23.8 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.Q5_K_M.gguf) | Q5_K_M | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.Q6_K.gguf) | Q6_K | 28.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF/resolve/main/Faro-Yi-34B-DPO.Q8_0.gguf) | Q8_0 | 36.6 | 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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