About
static quants of https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.1
weighted/imatrix quants are available at https://huggingface.co/mradermacher/FashionGPT-70B-V1.1-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 |
25.6 |
|
| GGUF |
IQ3_XS |
28.4 |
|
| GGUF |
IQ3_S |
30.0 |
beats Q3_K* |
| GGUF |
Q3_K_S |
30.0 |
|
| GGUF |
IQ3_M |
31.0 |
|
| GGUF |
Q3_K_M |
33.4 |
lower quality |
| GGUF |
Q3_K_L |
36.2 |
|
| GGUF |
IQ4_XS |
37.3 |
|
| GGUF |
Q4_K_S |
39.3 |
fast, recommended |
| GGUF |
Q4_K_M |
41.5 |
fast, recommended |
| GGUF |
Q5_K_S |
47.6 |
|
| GGUF |
Q5_K_M |
48.9 |
|
| PART 1 PART 2 |
Q6_K |
56.7 |
very good quality |
| PART 1 PART 2 |
Q8_0 |
73.4 |
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.