datasets:
- wenbopan/Fusang-v1
- wenbopan/OpenOrca-zh-20k
exported_from: wenbopan/Faro-Yi-34B-200K
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
About
weighted/imatrix quants of https://huggingface.co/wenbopan/Faro-Yi-34B-200K
This uses my "quarter" training set of 40k tokens as the model overflowed after 25k tokens with the standard set.
static quants are available at https://huggingface.co/mradermacher/Faro-Yi-34B-200K-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-IQ2_M | 12.5 | |
| GGUF | i1-Q2_K | 13.5 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 14.0 | lower quality |
| GGUF | i1-IQ3_XS | 14.9 | |
| GGUF | i1-Q3_K_S | 15.6 | IQ3_XS probably better |
| GGUF | i1-Q3_K_M | 17.3 | IQ3_S probably better |
| GGUF | i1-Q3_K_L | 18.8 | IQ3_M probably better |
| GGUF | i1-IQ4_XS | 19.1 | |
| GGUF | i1-Q4_0 | 20.2 | fast, low quality |
| GGUF | i1-Q4_K_S | 20.2 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 21.3 | fast, recommended |
| GGUF | i1-Q5_K_S | 24.3 | |
| GGUF | i1-Q5_K_M | 25.0 | |
| GGUF | i1-Q6_K | 28.9 | practically like static Q6_K |
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.