mradermacher/EVA-Qwen2.5-72B-v0.1-Padded-i1-GGUF

About

weighted/imatrix quants of https://huggingface.co/CalamitousFelicitousness/EVA-Qwen2.5-72B-v0.1-Padded

static quants are available at https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.1-Padded-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-IQ1_S	16.0	for the desperate
GGUF	i1-IQ1_M	17.5	mostly desperate
GGUF	i1-IQ2_XXS	19.9
GGUF	i1-IQ2_XS	22.0
GGUF	i1-IQ2_S	23.2
GGUF	i1-IQ2_M	25.1
GGUF	i1-Q2_K	27.4	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	28.6	lower quality
GGUF	i1-IQ3_XS	30.5
GGUF	i1-IQ3_S	32.1	beats Q3_K*
GGUF	i1-Q3_K_S	32.1	IQ3_XS probably better
GGUF	i1-IQ3_M	33.2
GGUF	i1-Q3_K_M	35.6	IQ3_S probably better
GGUF	i1-Q3_K_L	38.5	IQ3_M probably better
GGUF	i1-IQ4_XS	39.3
GGUF	i1-Q4_0	41.6	fast, low quality
GGUF	i1-Q4_K_S	41.9	optimal size/speed/quality
GGUF	i1-Q4_K_M	44.1	fast, recommended
PART 1 PART 2	i1-Q5_K_S	50.4
PART 1 PART 2	i1-Q5_K_M	51.8
PART 1 PART 2	i1-Q6_K	60.0	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

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.