mradermacher/deepseek-uncensored-lore-i1-GGUF

---

base_model: luvGPT/deepseek-uncensored-lore language:

• en

library_name: transformers quantized_by: mradermacher tags:

• text-generation
• storytelling
• transformers
• DeepSeek

---

About

weighted/imatrix quants of https://huggingface.co/luvGPT/deepseek-uncensored-lore

static quants are available at https://huggingface.co/mradermacher/deepseek-uncensored-lore-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	i1-IQ1_S	1.8	for the desperate
GGUF	i1-IQ1_M	1.9	mostly desperate
GGUF	i1-IQ2_XXS	2.1
GGUF	i1-IQ2_XS	2.3
GGUF	i1-IQ2_S	2.5
GGUF	i1-Q2K_S	2.6	very low quality
GGUF	i1-IQ2_M	2.6
GGUF	i1-Q2K	2.8	IQ3XXS probably better
GGUF	i1-IQ3_XXS	2.9	lower quality
GGUF	i1-IQ3_XS	3.1
GGUF	i1-IQ3S	3.2	beats Q3K*
GGUF	i1-Q3KS	3.2	IQ3XS probably better
GGUF	i1-IQ3_M	3.4
GGUF	i1-Q3KM	3.6	IQ3S probably better
GGUF	i1-Q3KL	3.8	IQ3M probably better
GGUF	i1-IQ4_XS	3.9
GGUF	i1-IQ4NL	4.1	prefer IQ4XS
GGUF	i1-Q4_0	4.1	fast, low quality
GGUF	i1-Q4K_S	4.1	optimal size/speed/quality
GGUF	i1-Q4K_M	4.3	fast, recommended
GGUF	i1-Q4_1	4.5
GGUF	i1-Q5K_S	4.9
GGUF	i1-Q5K_M	5.0
GGUF	i1-Q6K	5.8	practically like static Q6K

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 private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.