# Multimodal Q&A

This model is a GGUF format conversion of llava-hf/llava-1.5-7b-hf, supporting image-to-text generation tasks.

VL-Rethinker-7B 4-bit MLX Quantized Version is a quantized variant of the TIGER-Lab/VL-Rethinker-7B model, optimized for Apple devices and supporting visual question-answering tasks.

This model is a multimodal vision-language model converted from Qwen2.5-VL-7B-Instruct, supporting visual question answering tasks.

This model is a multimodal vision-language model converted from Qwen2.5-VL-7B-Instruct, supporting 8-bit quantization and suitable for visual question-answering tasks.

VL-Rethinker-72B-4bit is a multimodal model based on Qwen2.5-VL-7B-Instruct, supporting visual question answering tasks, and has been converted to MLX format for efficient operation on Apple devices.

This model is a GGUF format conversion of mlabonne/gemma-3-4b-it-abliterated, combined with the visual component of x-ray_alpha for a smoother multimodal experience.

LLaVAction is a multimodal large language model evaluation and training framework for action recognition, based on the Qwen2 language model architecture, supporting first-person perspective video understanding.

A multimodal video-text model built upon InternVideo2-1B and Qwen2.5-7B, using only 16 tokens per frame and supporting input sequences of up to 10,000 frames.

Asagi-8B is a large-scale Japanese Vision-Language Model (VLM) trained on extensive Japanese datasets, integrating diverse data sources.

This is the result of applying weight/importance matrix quantization to the EraX-VL-7B-V2.0-Preview model, offering multiple quantization versions to suit different needs

VideoChat-Flash-2B is a multimodal model built upon UMT-L (300M) and Qwen2.5-1.5B, supporting video-to-text tasks with only 16 tokens per frame and extending the context window to 128k.

EraX-VL-7B-V2.0-Preview is a powerful multimodal model designed for OCR and visual question answering, excelling in processing multiple languages including Vietnamese, with outstanding performance in recognizing medical forms, invoices, and other documents.

A multimodal model fine-tuned based on InternVL-Chat-V1-5, excelling in MMBench benchmark tests

Idefics2 is an open multimodal model capable of accepting arbitrary sequences of images and text as input and generating text output. The model can answer questions about images, describe visual content, create stories based on multiple images, or function purely as a language model.

A vision-language model capable of conversing about input images, supporting Japanese interaction

ChatTruth-7B is a multilingual vision-language model optimized based on the Qwen-VL architecture, enhanced with large-resolution image processing capabilities and incorporating a restoration module to reduce computational overhead

Heron GIT Japanese StableLM Base 7B is a vision-language model capable of conversing about input images.

IDEFICS is an open-source multimodal model capable of processing both image and text inputs to generate text outputs, serving as an open-source reproduction of Deepmind's Flamingo model.

A model for visual question answering tasks, focusing on understanding user interface reference expressions.