Qwen2.5-VL-7B-Abliterated-Caption-it-GGUF Open-source Image Captioning Model - Effortlessly Generate Multilingual Image Captions

Qwen2.5 VL 7B Abliterated Caption It GGUF

Developed by mradermacher

This is a static quantized version based on the Qwen2.5-VL-7B model, focusing on image captioning generation tasks and supporting multiple languages.

Image-to-Text

Transformers

Supports Multiple LanguagesOpen Source License:Apache-2.0 #Multimodal Image Captioning #Multilingual Support #Static Quantization

Downloads 133

Release Time : 7/23/2025

Model Overview

This model is a quantized version of prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it, mainly used for visual understanding tasks, especially image captioning generation. It supports English, Chinese, and Thai.

Model Features

Multilingual Support

Supports image captioning generation in three languages: English, Chinese, and Thai

Quantized Version

Provides multiple quantized versions, from Q2_K to f16, to meet different hardware and performance requirements

Visual Understanding

Focuses on visual understanding tasks, especially image-to-text conversion

Model Capabilities

Image Captioning Generation

Multilingual Text Generation

Visual Content Understanding

Use Cases

Content Generation

Automatic Image Annotation

Generate descriptive text for images

Can be used in scenarios such as social media and content management systems

Assistive Technology

Visual Assistance

Provide image content descriptions for visually impaired people

Improve information accessibility

🚀 Qwen2.5-VL-7B-Abliterated-Caption-it Quantized Model

This project provides static quantizations of the Qwen2.5-VL-7B-Abliterated-Caption-it model, offering various quantized versions for different usage scenarios.

📚 Documentation

About

Static quants of 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 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	Q3_K_S	3.6
GGUF	Q3_K_M	3.9	lower quality
GGUF	Q3_K_L	4.2
GGUF	IQ4_XS	4.4
GGUF	Q4_K_S	4.6	fast, recommended
GGUF	Q4_K_M	4.8	fast, recommended
GGUF	Q5_K_S	5.4
GGUF	Q5_K_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):

And here are Artefact2's thoughts on the matter: Artefact2's gist

FAQ / Model Request

See 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.

Model Information

Property	Details
Base Model	prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it
Datasets	prithivMLmods/blip3o-caption-mini-arrow, prithivMLmods/Caption3o-Opt-v2
Languages	en, zh, th
Library Name	transformers
License	apache - 2.0
Quantized By	mradermacher
Tags	text-generation-inference, uncensored, image-captioning, vlm, visual-understanding, caption, image-to-text