mradermacher/DeepSeek-V2-Lite-GGUF

---

base_model: ZZichen/DeepSeek-V2-Lite language:

• en

library_name: transformers noimatrix: 'GGMLASSERT: llama.cpp/ggml-cuda/concat.cu:107: ggmliscontiguous(src0)' quantized_by: mradermacher

---

About

static quants of https://huggingface.co/ZZichen/DeepSeek-V2-Lite

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	6.5
GGUF	IQ3_XS	7.2
GGUF	IQ3S	7.6	beats Q3K*
GGUF	Q3K_S	7.6
GGUF	IQ3_M	7.7
GGUF	Q3K_M	8.2	lower quality
GGUF	Q3K_L	8.6
GGUF	IQ4_XS	8.7
GGUF	Q4K_S	9.6	fast, recommended
GGUF	Q4K_M	10.5	fast, recommended
GGUF	Q5K_S	11.2
GGUF	Q5K_M	12.0
GGUF	Q6_K	14.2	very good quality
GGUF	Q8_0	16.8	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.