mradermacher/Qwen2.5-VL-7B-Abliterated-Caption-it-GGUF

---

base_model: prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it datasets:

• prithivMLmods/blip3o-caption-mini-arrow
• prithivMLmods/Caption3o-Opt-v2

language:

• en
• zh
• th

library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags:

• text-generation-inference
• uncensored
• image-captioning
• vlm
• visual-understanding
• caption
• image-to-text

---

About

static quants of https://huggingface.co/prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it

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

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Abliterated-Caption-it-i1-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	mmproj-Q8_0	1.0	multi-modal supplement
GGUF	mmproj-f16	1.5	multi-modal supplement
GGUF	Q2_K	3.1
GGUF	Q3K_S	3.6
GGUF	Q3K_M	3.9	lower quality
GGUF	Q3K_L	4.2
GGUF	IQ4_XS	4.4
GGUF	Q4K_S	4.6	fast, recommended
GGUF	Q4K_M	4.8	fast, recommended
GGUF	Q5K_S	5.4
GGUF	Q5K_M	5.5
GGUF	Q6_K	6.4	very good quality
GGUF	Q8_0	8.2	fast, best quality
GGUF	f16	15.3	16 bpw, overkill

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.