mradermacher/Huihui-Kimi-Linear-48B-A3B-Instruct-abliterated-i1-GGUF

---

base_model: huihui-ai/Huihui-Kimi-Linear-48B-A3B-Instruct-abliterated language:

• en

library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher tags:

• abliterated
• uncensored

---

About

weighted/imatrix quants of https://huggingface.co/huihui-ai/Huihui-Kimi-Linear-48B-A3B-Instruct-abliterated

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

static quants are available at https://huggingface.co/mradermacher/Huihui-Kimi-Linear-48B-A3B-Instruct-abliterated-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	imatrix	0.3	imatrix file (for creating your own quants)
GGUF	i1-IQ1_S	10.2	for the desperate
GGUF	i1-IQ1_M	11.3	mostly desperate
GGUF	i1-IQ2_XXS	13.1
GGUF	i1-IQ2_XS	14.6
GGUF	i1-IQ2_S	14.8
GGUF	i1-IQ2_M	16.2
GGUF	i1-Q2K_S	16.8	very low quality
GGUF	i1-Q2K	18.1	IQ3XXS probably better
GGUF	i1-IQ3_XXS	19.1	lower quality
GGUF	i1-IQ3_XS	20.3
GGUF	i1-IQ3S	21.4	beats Q3K*
GGUF	i1-Q3KS	21.4	IQ3XS probably better
GGUF	i1-IQ3_M	21.6
GGUF	i1-Q3KM	23.6	IQ3S probably better
GGUF	i1-Q3KL	25.6	IQ3M probably better
GGUF	i1-IQ4_XS	26.4
GGUF	i1-Q4_0	28.0	fast, low quality
GGUF	i1-Q4K_S	28.1	optimal size/speed/quality
GGUF	i1-Q4K_M	29.8	fast, recommended
GGUF	i1-Q4_1	30.9
GGUF	i1-Q5K_S	34.0
GGUF	i1-Q5K_M	35.0
GGUF	i1-Q6K	40.5	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.