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metadata
base_model: deepseek-ai/DeepSeek-V2-Chat
language:
  - en
library_name: transformers
license: other
license_link: https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL
license_name: deepseek
quantized_by: mradermacher

About

weighted/imatrix quants of https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat

static quants are available at https://huggingface.co/mradermacher/DeepSeek-V2-Chat-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 i1-IQ2_M 77.0
PART 1 PART 2 i1-Q2_K 86.0 IQ3_XXS probably better
PART 1 PART 2 i1-IQ3_XXS 90.9 lower quality
PART 1 PART 2 PART 3 i1-Q3_K_S 101.8 IQ3_XS probably better
PART 1 PART 2 PART 3 i1-Q3_K_M 112.8 IQ3_S probably better
PART 1 PART 2 PART 3 i1-IQ4_XS 125.7
PART 1 PART 2 PART 3 i1-Q4_K_S 134.0 optimal size/speed/quality
PART 1 PART 2 PART 3 i1-Q4_K_M 142.6 fast, recommended
PART 1 PART 2 PART 3 PART 4 i1-Q6_K 193.6 practically like static Q6_K

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. Additional thanks to @nicoboss for giving me access to his hardware for calculating the imatrix for these quants.