# Multimodal Visual Question Answering

FP8 quantized version of Qwen2.5-VL-72B-Instruct, supporting vision-text input and text output, optimized and released by Neural Magic.

The quantized version of Qwen2.5-VL-3B-Instruct, with weights quantized to INT4 and activations quantized to FP16, designed for efficient vision-text task inference.

The FP8 quantized version of Qwen2.5-VL-72B-Instruct, supporting vision-text input and text output, suitable for multimodal tasks.

A quantized version of the multimodal model based on Qwen2-VL-7B-Instruct, supporting image-text-to-text tasks with various quantization levels.

EraX-VL-7B-V2.0-Preview is a multimodal foundation model supporting Vietnamese, English, and Chinese, suitable for various vision-language tasks.

This is a multimodal visual question answering model supporting Vietnamese, English, and Chinese, converted to GGUF format based on erax-ai/EraX-VL-2B-V1.5.

QVQ-72B-Preview is a multimodal large language model based on the imatrix quantization version of llamacpp, supporting multimodal understanding and generation of images and text.

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

PaliGemma 2 is a Vision-Language Model (VLM) launched by Google, combining the capabilities of the Gemma 2 language model and SigLIP vision model, supporting image and text inputs to generate text outputs.

PaliGemma 2 is an upgraded vision-language model developed by Google, combining the capabilities of Gemma 2, supporting image and text inputs to generate text outputs, suitable for various vision-language tasks.

MiniCPM-Llama3-V-2_5 is a multimodal visual question answering model based on the Llama3 architecture, supporting both Chinese and English interactions.

This project trained a projection module to add visual capabilities to Llama 3 using SigLIP technology, applied to the Llama-3.1-8B-Instruct model.

Yi-VL-6B is a multimodal vision-language model developed by 01-AI, supporting both Chinese and English, suitable for tasks like visual question answering.

PaliGemma is a 3B-parameter lightweight vision-language model developed by Google, built upon SigLIP vision model and Gemma language model, supporting image and text inputs to generate text outputs.

PaliGemma is a versatile lightweight vision-language model (VLM) built upon the SigLIP vision model and Gemma language model, supporting image and text inputs to generate text outputs

PaliGemma is a lightweight vision-language model developed by Google, built on the SigLIP vision model and the Gemma language model, supporting multilingual image-text understanding and generation.

PaliGemma is a lightweight vision-language model developed by Google, combining image understanding and text generation capabilities, supporting multilingual tasks.

PaliGemma is a versatile lightweight vision-language model (VLM) developed by Google, built on the SigLIP vision model and Gemma language model, supporting both image and text inputs with text outputs.

FireLLaVA-13B is a vision-language model trained on instruction data generated by open-source large language models, supporting image understanding and text generation tasks.