mradermacher/Qwen2.5-3X7B-CoderInstruct-OlympicCoder-MS-Next-Coder-25B-v1-128k-ctx-GGUF

About

static quants of https://huggingface.co/DavidAU/Qwen2.5-3X7B-CoderInstruct-OlympicCoder-MS-Next-Coder-25B-v1-128k-ctx

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-3X7B-CoderInstruct-OlympicCoder-MS-Next-Coder-25B-v1-128k-ctx-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	9.3
GGUF	Q3_K_S	10.9
GGUF	Q3_K_M	12.0	lower quality
GGUF	Q3_K_L	13.0
GGUF	IQ4_XS	13.5
GGUF	Q4_K_S	14.3	fast, recommended
GGUF	Q4_K_M	15.1	fast, recommended
GGUF	Q5_K_S	17.2
GGUF	Q5_K_M	17.7
GGUF	Q6_K	20.4	very good quality
GGUF	Q8_0	26.4	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.