# Multimodal Visual Understanding

Qwen2.5-VL is the latest vision-language model in the Qwen family, featuring powerful visual understanding and multimodal processing capabilities.

The Perception Encoder is a state-of-the-art image and video understanding encoder trained through vision-language training, with strong generalization capabilities.

The Perception Encoder (PE) is an advanced image and video understanding encoder trained through vision-language learning, achieving state-of-the-art performance on various visual tasks.

Qwen2.5-VL-32B-Instruct is the latest vision-language model in the Qwen family, featuring powerful visual understanding and intelligent agent capabilities, supporting multimodal task processing.

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

Qwen2.5-VL is the latest vision-language model in the Qwen family, focusing on enhanced visual understanding and agent capabilities.

This is a quantized version based on the mlabonne/gemma-3-4b-it-abliterated model, using llama.cpp for imatrix quantization, suitable for image-text-to-text tasks.

This is a weighted/importance matrix quantized version of the Minthy/ToriiGate-v0.4-7B model, offering multiple quantization options to suit different needs.

Qwen2.5-VL is the latest vision-language model in the Qwen family, featuring powerful visual understanding and agent capabilities, supporting multi-format visual localization and structured output generation.

Qwen2.5-VL is a multimodal large language model launched by the QwenLM team, featuring powerful visual understanding and intelligent agent capabilities, supporting various input formats including images, videos, and text.

Qwen2.5-VL is a multimodal vision-language model launched by Tongyi Qianwen, featuring powerful image understanding and text generation capabilities.

MiniCPM-o 2.6 is a multimodal model that supports vision and language tasks, specifically designed for llama.cpp.

Razorback 12B v0.2 is a multimodal model combining the strengths of Pixtral 12B and UnslopNemo v3, featuring visual understanding and language processing capabilities.

Meta Llama 3.2 series 90B-parameter multimodal large language model supporting visual instruction understanding, optimized with Unsloth dynamic 4-bit quantization

MiniCPM-V 2.6 is a GPT-4V-level multimodal large language model supporting single-image, multi-image, and video understanding, optimized for RK3588 NPU

Cambrian is an open-source multimodal LLM (Large Language Model) designed with a vision-centric approach.

Phi-3-Vision-128K-Instruct is a lightweight, cutting-edge open multimodal model supporting a 128K token context length, focusing on high-quality reasoning in text and visual domains.

A vision-language model that combines the Phi-3-mini-3.8B large language model with LLaVA v1.5, providing advanced vision-language understanding capabilities.

OWLv2 is a vision-language pre-trained model focused on object detection and localization tasks.