About
static quants of https://huggingface.co/fblgit/UNA-POLAR-10.7B-InstructMath-v2
weighted/imatrix quants are available at https://huggingface.co/mradermacher/UNA-POLAR-10.7B-InstructMath-v2-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 | 4.1 | |
GGUF | Q3_K_S | 4.8 | |
GGUF | Q3_K_M | 5.3 | lower quality |
GGUF | Q3_K_L | 5.8 | |
GGUF | IQ4_XS | 5.9 | |
GGUF | Q4_0_4_4 | 6.2 | fast on arm, low quality |
GGUF | Q4_K_S | 6.2 | fast, recommended |
GGUF | Q4_K_M | 6.6 | fast, recommended |
GGUF | Q5_K_S | 7.5 | |
GGUF | Q5_K_M | 7.7 | |
GGUF | Q6_K | 8.9 | very good quality |
GGUF | Q8_0 | 11.5 | fast, best quality |
GGUF | f16 | 21.6 | 16 bpw, overkill |
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. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
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Model tree for mradermacher/UNA-POLAR-10.7B-InstructMath-v2-GGUF
Base model
fblgit/UNA-POLAR-10.7B-InstructMath-v2