mradermacher/Qwen2.5-72B-Instruct-GGUF

About

static quants of https://huggingface.co/Qwen/Qwen2.5-72B-Instruct

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-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	29.9
GGUF	IQ3_XS	32.9
GGUF	IQ3_S	34.6	beats Q3_K*
GGUF	Q3_K_S	34.6
GGUF	IQ3_M	35.6
GGUF	Q3_K_M	37.8	lower quality
GGUF	Q3_K_L	39.6
GGUF	IQ4_XS	40.3
GGUF	Q4_0	41.3	fast, low quality
GGUF	Q4_0_4_4	41.3	fast on arm, low quality
GGUF	Q4_0_4_8	41.3	fast on arm+i8mm, low quality
GGUF	Q4_0_8_8	41.3	fast on arm+sve, low quality
GGUF	IQ4_NL	41.9	prefer IQ4_XS
GGUF	Q4_K_S	44.0	fast, recommended
GGUF	Q4_1	45.8
GGUF	Q4_K_M	47.5	fast, recommended
PART 1 PART 2	Q5_0	50.3
PART 1 PART 2	Q5_K_S	51.5
PART 1 PART 2	Q5_K_M	54.5
PART 1 PART 2	Q5_1	54.7
PART 1 PART 2	Q6_K	64.4	very good quality
PART 1 PART 2	Q8_0	77.4	fast, best quality
PART 1 PART 2 PART 3	SOURCE	145.5	source gguf, only provided when it was hard to come by

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.