TreeVGR-7B-CI-GGUF Open-Source Visual Model - Free Deployment for Question-Answering, Localization, and Inference Tasks

Treevgr 7B CI GGUF

Developed by mradermacher

The static quantized version of TreeVGR-7B-CI, suitable for tasks such as visual question answering, visual localization, and visual reasoning.

Text-to-Image

Transformers

EnglishOpen Source License:Apache-2.0 #Visual Question Answering Quantization #Multimodal Reasoning #Low-Resource Deployment

Downloads 150

Release Time : 7/12/2025

Model Overview

This project provides a static quantized version of the HaochenWang/TreeVGR-7B-CI model, supporting tasks such as visual question answering, visual localization, and visual reasoning.

Model Features

Static Quantization

Provides multiple quantized versions suitable for different hardware and performance requirements.

Multimodal Support

Supports multimodal task processing of vision and language.

Efficient Inference

The quantized version significantly reduces the model size and inference time while maintaining high performance.

Model Capabilities

Visual Question Answering

Visual Localization

Visual Reasoning

Multimodal Understanding

Use Cases

Visual Question Answering

Image Content Question Answering

Answer relevant questions based on the image content.

Visual Localization

Image Region Localization

Locate specific regions in the image based on the description.

Visual Reasoning

Image Logical Reasoning

Conduct logical reasoning based on the image content.

🚀 TreeVGR-7B-CI Quantized Model

This project provides static quantizations of the TreeVGR-7B-CI model. It offers various quantized versions for different usage scenarios, facilitating visual question - answering, visual grounding, and visual reasoning tasks.

📚 Documentation

Model Information

Property	Details
Base Model	HaochenWang/TreeVGR-7B-CI
Datasets	HaochenWang/TreeBench, HaochenWang/TreeVGR-RL-37K, HaochenWang/TreeVGR-SFT-35K
Language	en
Library Name	transformers
License	apache - 2.0
Quantized By	mradermacher
Tags	visual-question-answering, visual-grounding, visual-reasoning, qwen

About

Static quants of HaochenWang/TreeVGR-7B-CI.

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

Weighted/imatrix quants are available at mradermacher/TreeVGR-7B-CI-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.

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