exported_from: deepnight-research/saily_100b
language:
- en
library_name: transformers
license: mit
license_link: LICENSE
license_name: deepnight-responsible-ai
quantized_by: mradermacher
About
static quants of https://huggingface.co/deepnight-research/saily_100b
weighted/imatrix quants are available at https://huggingface.co/mradermacher/saily_100b-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 | 43.7 | |
| GGUF | IQ3_XS | 48.4 | |
| PART 1 PART 2 | Q3_K_S | 51.1 | |
| PART 1 PART 2 | IQ3_S | 51.3 | beats Q3_K* |
| PART 1 PART 2 | IQ3_M | 53.0 | |
| PART 1 PART 2 | Q3_K_M | 57.0 | lower quality |
| PART 1 PART 2 | Q3_K_L | 62.1 | |
| PART 1 PART 2 | IQ4_XS | 63.9 | |
| PART 1 PART 2 | Q4_K_S | 67.2 | fast, recommended |
| PART 1 PART 2 | Q4_K_M | 71.0 | fast, recommended |
| PART 1 PART 2 | Q5_K_S | 81.4 | |
| PART 1 PART 2 | Q5_K_M | 83.6 | |
| PART 1 PART 2 PART 3 | Q6_K | 97.0 | very good quality |
| PART 1 PART 2 PART 3 | Q8_0 | 125.5 | 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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.