base_model: deepnight-research/Saily_220B
datasets:
- tiiuae/falcon-refinedweb
- EleutherAI/pile
- meta-math/MetaMathQA
language:
- en
library_name: transformers
license: llama2
no_imatrix: 'GGML_ASSERT: llama.cpp/ggml.c:16553: i != GGML_HASHTABLE_FULL'
quantized_by: mradermacher
About
static quants of https://huggingface.co/deepnight-research/Saily_220B
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 |
|---|---|---|---|
| PART 1 PART 2 | Q2_K | 76.9 | |
| PART 1 PART 2 | IQ3_XS | 85.5 | |
| PART 1 PART 2 | Q3_K_S | 90.1 | |
| PART 1 PART 2 | IQ3_S | 90.4 | beats Q3_K* |
| PART 1 PART 2 | IQ3_M | 93.5 | |
| PART 1 PART 2 PART 3 | Q3_K_M | 100.6 | lower quality |
| PART 1 PART 2 PART 3 | Q3_K_L | 109.5 | |
| PART 1 PART 2 PART 3 | IQ4_XS | 112.7 | |
| PART 1 PART 2 PART 3 | Q4_0 | 117.7 | fast, low quality |
| PART 1 PART 2 PART 3 | Q4_K_S | 118.6 | fast, recommended |
| PART 1 PART 2 PART 3 | Q4_K_M | 125.3 | fast, recommended |
| PART 1 PART 2 PART 3 | Q5_K_S | 143.8 | |
| PART 1 PART 2 PART 3 PART 4 | Q5_K_M | 147.7 | |
| PART 1 PART 2 PART 3 PART 4 | Q6_K | 171.4 | very good quality |
| P1 P2 P3 P4 P5 | Q8_0 | 221.8 | 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.