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
weighted/imatrix quants of https://huggingface.co/wenbopan/Faro-Yi-34B-DPO
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 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 | i1-Q2_K | 12.9 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 13.4 | lower quality |
| GGUF | i1-Q3_K_M | 16.8 | IQ3_S probably better |
| 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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.