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About

static quants of https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct

weighted/imatrix quants are available at https://huggingface.co/mradermacher/DeepSeek-Coder-V2-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
PART 1 PART 2 Q2_K 86.0
PART 1 PART 2 IQ3_XS 96.4
PART 1 PART 2 PART 3 IQ3_S 101.8 beats Q3_K*
PART 1 PART 2 PART 3 Q3_K_S 101.8
PART 1 PART 2 PART 3 IQ3_M 103.5
PART 1 PART 2 PART 3 Q3_K_M 112.8 lower quality
PART 1 PART 2 PART 3 Q3_K_L 122.5
PART 1 PART 2 PART 3 IQ4_XS 126.9
PART 1 PART 2 PART 3 Q4_K_S 134.0 fast, recommended
PART 1 PART 2 PART 3 Q4_K_M 142.6 fast, recommended
PART 1 PART 2 PART 3 PART 4 Q5_K_S 162.4
PART 1 PART 2 PART 3 PART 4 Q5_K_M 167.3
PART 1 PART 2 PART 3 PART 4 Q6_K 193.6 very good quality
P1 P2 P3 P4 P5 P6 Q8_0 250.7 fast, best quality

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png

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.

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