mradermacher/gemma3-27b-abliterated-dpo-i1-GGUF

---

base_model: summykai/gemma3-27b-abliterated-dpo datasets:

• mlabonne/orpo-dpo-mix-40k

language:

• en

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

• text-generation-inference
• transformers
• unsloth
• gemma3

---

About

weighted/imatrix quants of https://huggingface.co/summykai/gemma3-27b-abliterated-dpo

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

static quants are available at https://huggingface.co/mradermacher/gemma3-27b-abliterated-dpo-GGUF

This is a vision model - mmproj files (if any) will be in the static repository.

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	6.4	for the desperate
GGUF	i1-IQ1_M	6.9	mostly desperate
GGUF	i1-IQ2_XXS	7.8
GGUF	i1-IQ2_XS	8.5
GGUF	i1-IQ2_S	8.9
GGUF	i1-IQ2_M	9.6
GGUF	i1-Q2K_S	9.9	very low quality
GGUF	i1-Q2K	10.6	IQ3XXS probably better
GGUF	i1-IQ3_XXS	10.8	lower quality
GGUF	i1-IQ3_XS	11.7
GGUF	i1-IQ3S	12.3	beats Q3K*
GGUF	i1-Q3KS	12.3	IQ3XS probably better
GGUF	i1-IQ3_M	12.6
GGUF	i1-Q3KM	13.5	IQ3S probably better
GGUF	i1-Q3KL	14.6	IQ3M probably better
GGUF	i1-IQ4_XS	14.9
GGUF	i1-Q4_0	15.7	fast, low quality
GGUF	i1-Q4K_S	15.8	optimal size/speed/quality
GGUF	i1-Q4K_M	16.6	fast, recommended
GGUF	i1-Q4_1	17.3
GGUF	i1-Q5K_S	18.9
GGUF	i1-Q5K_M	19.4
GGUF	i1-Q6K	22.3	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.