# Multimodal Image-Text Understanding

Open-Qwen2VL is a multimodal model capable of receiving both images and text as input and generating text output.

Qwen2.5-VL-3B-Instruct is a 3B-parameter vision-language model that supports image-to-text generation tasks.

Qwen2.5-VL-7B-Instruct is a 7B-parameter multimodal vision-language model that supports joint understanding and generation tasks for images and text.

The GPTQ-Int3 quantized version of Qwen2.5-VL-3B-Instruct, suitable for multimodal image-text processing tasks with reduced VRAM usage and faster inference speed.

This is an unofficial GPTQ-Int3 quantized version based on the Qwen2.5-VL-7B-Instruct model, suitable for multimodal image-text-to-text tasks.

PaliGemma 2 is an upgraded vision-language model based on Gemma 2, supporting multilingual image-text input and text output, specifically designed for vision-language tasks

PaliGemma 2 is a vision-language model based on Gemma 2, supporting image and text inputs to generate text outputs, suitable for various vision-language tasks.

LLaVA-1.6 is an open-source vision-language model that supports image-text-to-text tasks, with improved visual understanding and text generation capabilities.

BLIP is an advanced vision-language pretraining model, excelling in image captioning tasks by effectively utilizing web data through guided annotation strategies