Dermatech-Qwen2-VL-2B-i1-GGUF Open-Source Multimodal Model - Supports Text Generation, Image-to-Text, and Question Answering

Dermatech Qwen2 VL 2B I1 GGUF

Developed by mradermacher

This is a multimodal model based on the Qwen2 architecture, focusing on text generation, image-to-text, and visual question answering tasks.

Image-to-Text English#Multimodal Medical Analysis #Extremely Low-bit Quantization Inference #Dermatology Visual Question Answering

Downloads 60

Release Time : 12/15/2024

Model Overview

Dermatech-Qwen2-VL-2B is a 2B-parameter multimodal model supporting text generation, image-to-text, and visual question answering tasks. It is a quantized version of the original model, suitable for resource-constrained environments.

Model Features

Multimodal Capability

Supports joint processing of text and images, enabling visual question answering and image caption generation tasks.

Quantized Version

Offers multiple quantized versions suitable for scenarios with varying hardware resource constraints.

Efficient Inference

Achieves more efficient inference speed through quantization techniques, ideal for edge device deployment.

Model Capabilities

Text generation

Image-to-text

Visual question answering

Multimodal processing

Use Cases

Medical Image Analysis

Dermatology Diagnosis Assistance

Analyzes skin lesion images and generates descriptive reports

Education

Visual Question Answering System

Answers natural language questions about image content

🚀 Dermatech-Qwen2-VL-2B Quantized Model

This project provides quantized versions of the Dermatech-Qwen2-VL-2B model for various applications, including text generation, image-to-text conversion, and visual question-answering.

📚 Documentation

Model Information

Property	Details
Base Model	Rewatiramans/Dermatech-Qwen2-VL-2B
Language	en
Library Name	transformers
Quantized By	mradermacher
Tags	text-generation, image-to-text, visual-question-answering, multimodal, qwen, lora

About

Weighted/imatrix quants of https://huggingface.co/Rewatiramans/Dermatech-Qwen2-VL-2B. Static quants are available at https://huggingface.co/mradermacher/Dermatech-Qwen2-VL-2B-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	i1-IQ1_S	0.5	for the desperate
GGUF	i1-IQ1_M	0.6	mostly desperate
GGUF	i1-IQ2_XXS	0.6
GGUF	i1-IQ2_XS	0.7
GGUF	i1-IQ2_S	0.7
GGUF	i1-IQ2_M	0.7
GGUF	i1-Q2_K_S	0.7	very low quality
GGUF	i1-IQ3_XXS	0.8	lower quality
GGUF	i1-Q2_K	0.8	IQ3_XXS probably better
GGUF	i1-IQ3_XS	0.8
GGUF	i1-Q3_K_S	0.9	IQ3_XS probably better
GGUF	i1-IQ3_S	0.9	beats Q3_K*
GGUF	i1-IQ3_M	0.9
GGUF	i1-Q3_K_M	0.9	IQ3_S probably better
GGUF	i1-Q3_K_L	1.0	IQ3_M probably better
GGUF	i1-IQ4_XS	1.0
GGUF	i1-IQ4_NL	1.0	prefer IQ4_XS
GGUF	i1-Q4_0	1.0	fast, low quality
GGUF	i1-Q4_K_S	1.0	optimal size/speed/quality
GGUF	i1-Q4_K_M	1.1	fast, recommended
GGUF	i1-Q4_1	1.1
GGUF	i1-Q5_K_S	1.2
GGUF	i1-Q5_K_M	1.2
GGUF	i1-Q6_K	1.4	practically like static Q6_K

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: 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.

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