base_model: wenbopan/Faro-Yi-34B-DPO
datasets:
- wenbopan/Chinese-dpo-pairs
- Intel/orca_dpo_pairs
- argilla/ultrafeedback-binarized-preferences-cleaned
- jondurbin/truthy-dpo-v0.1
language:
- en
- zh
library_name: transformers
license: mit
quantized_by: mradermacher
About
static quants of https://huggingface.co/wenbopan/Faro-Yi-34B-DPO
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs 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 | Q2_K | 12.9 | |
| GGUF | IQ3_XS | 14.3 | |
| GGUF | Q3_K_S | 15.1 | |
| GGUF | IQ3_S | 15.1 | beats Q3_K* |
| GGUF | IQ3_M | 15.7 | |
| GGUF | Q3_K_M | 16.8 | lower quality |
| GGUF | Q3_K_L | 18.2 | |
| GGUF | IQ4_XS | 18.7 | |
| GGUF | Q4_K_S | 19.7 | fast, recommended |
| GGUF | Q4_K_M | 20.8 | fast, recommended |
| GGUF | Q5_K_S | 23.8 | |
| GGUF | Q5_K_M | 24.4 | |
| GGUF | Q6_K | 28.3 | very good quality |
| 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
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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.