# Visual Question Answering

SpaceOm-GGUF is a multimodal model focusing on visual question answering tasks and performs excellently in spatial reasoning.

Gemma 3 is Google's lightweight open model series based on Gemini technology. The 12B version employs Quantization-Aware Training (QAT) technology, supports multimodal input, and features a 128K context window.

GIT is a Transformer-based image-to-text generation model capable of generating descriptive text from input images.

VoRA is a vision-language model based on 7B parameters, focusing on image-text-to-text conversion tasks.

Sapnous-6B is an advanced vision-language model that enhances perception and understanding of the world through powerful multimodal capabilities.

Gemma 3 is a lightweight open-source multimodal model series launched by Google, built on the same technology as Gemini, supporting text and image inputs and generating text outputs

Gemma is a lightweight cutting-edge open model series launched by Google, built on the same technology as Gemini, supporting multimodal input and text output.

A lightweight multimodal model designed for analyzing video content, capable of processing video, image, and text inputs to generate text outputs.

SmolVLM2-256M-Video is a lightweight multimodal model specifically designed for analyzing video content, capable of processing video, image, and text inputs to generate text outputs.

Quantized version of Qwen2.5-VL-7B-Instruct, supporting vision-text input and text output, optimized for inference efficiency through INT8 weight quantization

The FP8 quantized version of Qwen2.5-VL-7B-Instruct, supporting efficient vision-text inference through vLLM

The FP8 quantized version of Qwen2.5-VL-3B-Instruct, supporting visual-text input and text output, and optimizing inference efficiency.

LlamaV-o1 is an advanced multimodal large language model specifically designed for complex visual reasoning tasks, optimized through curriculum learning techniques, demonstrating outstanding performance across diverse benchmarks.

GIT is a Transformer-based generative image-to-text model capable of converting visual content into textual descriptions.

PaliGemma 2 is a multimodal vision-language model that combines image and text inputs to generate text outputs. It supports multiple languages and is suitable for various vision-language tasks.

A dermatology-specific diagnostic model fine-tuned using LoRA technology based on Qwen2-VL-2B-Instruct, capable of analyzing skin condition images and providing professional diagnostic descriptions.

A lung cancer detection model fine-tuned based on Florence-2-base-ft, identifying lung cancer types through lung images

The Peacock Model is an Arabic multimodal large language model based on the InstructBLIP architecture, with AraLLaMA as its language model.

GUIChat is a multimodal model based on Visual Question Answering (VQA), capable of understanding image content and answering related questions, specifically optimized for GUI element recognition and interaction.

Donut is a Transformer-based image-to-text model capable of extracting and generating textual content from images.

A multimodal dialogue model fine-tuned based on google/paligemma-3b-mix-448, optimized for multi-turn conversation scenarios

This model is a fine-tuned version of google/paligemma-3b-pt-224 on a subset of the VQAv2 dataset, specializing in visual question answering tasks.

360VL is a multimodal model developed based on the LLama3 language model, featuring powerful image understanding and bilingual dialogue capabilities.

A large multimodal model trained based on the LLaVA-v1.5 framework, using the 8-billion-parameter Meta-Llama-3-8B-Instruct as the language backbone and equipped with a CLIP-based visual encoder.

LLaVA-Next-Video is an open-source multimodal dialogue model, fine-tuned with multimodal instruction-following data on large language models, supporting video and text multimodal interactions.

UForm-Gen2-dpo is a small generative vision-language model, aligned for image caption generation and visual question answering tasks through Direct Preference Optimization (DPO) on VLFeedback and LLaVA-Human-Preference-10K preference datasets.

MoAI is a large-scale language and vision hybrid model capable of processing both image and text inputs to generate text outputs.

LLaVA is a large language and vision assistant model capable of handling multimodal tasks involving images and text.

LLaVA is a vision-language model capable of understanding and generating text related to images.

LLaVA-v1.5-13B-DPO is a vision-language model based on the LLaVA framework, trained with Direct Preference Optimization (DPO) and converted to GGUF quantized format to improve inference efficiency.

LLaVA 1.6 34B is an open-source multimodal chatbot model developed by fine-tuning a large language model on multimodal instruction-following data. It supports image-to-text and text-to-text generation tasks.

LLaVA is an open-source multimodal chatbot, fine-tuned on large language models with multimodal instruction-following data.

LLaVA is an open-source multimodal chatbot, trained by fine-tuning large language models on multimodal instruction-following data.

MiniCPM-V is an efficient lightweight multimodal model optimized for edge device deployment, supporting bilingual (Chinese-English) interaction and outperforming models of similar scale.

A 1.6B-parameter multimodal model combining SigLIP and Phi-1.5 architectures, supporting image understanding and Q&A tasks

BLIP2 is a vision-language model based on OPT-2.7B, focusing on visual question answering tasks. It can understand image content and answer related questions.

InfiMM is a multimodal vision-language model inspired by the Flamingo architecture, integrating the latest LLM models and suitable for a wide range of vision-language processing tasks.

UForm-Gen-Chat is the fine-tuned multimodal conversational version of UForm-Gen, primarily used for image caption generation and visual question answering tasks.

UForm-Gen is a small generative vision-language model primarily used for image caption generation and visual question answering.

Yi-VL-34B is an open-source multimodal model from the Yi series, capable of understanding image content and engaging in multi-turn conversations, with outstanding performance on the MMMU and CMMMU benchmarks.