CodeV is a 7-billion-parameter vision-language model fine-tuned based on Qwen2.5-VL-7B-Instruct. Through two-stage training of supervised fine-tuning (SFT) and reinforcement learning (RL) based on tool-aware policy optimization (TAPO), it aims to achieve reliable and interpretable visual reasoning. It represents visual tools as executable Python code and ensures the consistency between tool usage and question evidence through a reward mechanism, solving the problem of irrelevant tool invocation under high accuracy.

MultimodalTransformersMultiple Languages

This is an object detection model fine-tuned on a custom dataset based on the YOLOv12 Medium architecture. This model is specifically designed to efficiently and accurately detect drone targets in images or videos, providing support for computer vision applications.

Qwen-SEA-LION-v4-4B-VL is a vision-language model with 4 billion parameters built on the Qwen3-VL-4B-Instruct architecture. It has been specifically instruction fine-tuned for the Southeast Asian region, possessing multilingual and multicultural capabilities, supporting English and seven Southeast Asian languages, and retaining strong vision-language understanding capabilities.

MultimodalTransformersMultiple Languages

The MiMo Embodied Model (MiMo-Embodied) is a powerful cross-embodied vision-language model that has demonstrated excellent performance in both autonomous driving and embodied AI tasks. It is the first open-source vision-language model that combines these two key domains, significantly enhancing the understanding and reasoning abilities in dynamic physical environments.

MultimodalTransformers

Sarashina2.2-Vision-3B is a large Japanese vision-language model trained by SB Intuitions. It is built based on Sarashina2.2-3B-Instruct and the SigLIP image encoder, and has powerful image-to-text conversion capabilities, supporting multimodal processing in Japanese and English.

MultimodalTransformersMultiple Languages

Jan-v2-VL is a vision-language model with 8 billion parameters, designed specifically for long-term, multi-step tasks in real software environments (such as browsers and desktop applications). It combines language reasoning with visual perception, can follow complex instructions, maintain intermediate states, and recover from minor execution errors.

MultimodalGguf

Jan-v2-VL is a vision-language model with 8 billion parameters, designed specifically for executing long-term, multi-step tasks in real software environments such as browsers and desktop applications. It tightly integrates language reasoning with visual perception, can follow complex instructions, maintain intermediate states, and recover from minor execution errors to achieve stable and continuous task completion.

MultimodalTransformersEnglish

Moondream 3 Preview HF is a re-implementation of the Moondream 3 (Preview) model based on the HuggingFace Transformers architecture specification, making it fully compatible with the Hugging Face ecosystem. It is a multimodal vision-language model that uses a Mixture of Experts (MoE) text backbone, with approximately 9 billion parameters and 2 billion active parameters.

MultimodalTransformers

This is an enhanced multimodal vision-language model based on the Qwen3-VL-8B-Thinking model. It is extended to 12B parameters through Brainstorm 20x technology and uses NEO Imatrix-enhanced GGUF quantization. The model has powerful image understanding, text generation, and multimodal reasoning capabilities, with significant improvements in visual perception, text quality, and creative scenarios.

MultimodalGgufMultiple Languages

NV-Reason-CXR-3B GGUF is a quantized version of the NVIDIA NV-Reason-CXR-3B vision-language model, optimized for edge device deployment. This is a model with 3 billion parameters, focusing on chest X-ray analysis. It has been converted to the GGUF format and quantized for efficient operation on mobile devices, desktops, and embedded systems.

MultimodalGgufEnglish

Qwen3-VL-4B-Instruct is the latest vision-language model in the Tongyi Qianwen series. It achieves excellent visual perception, text understanding and generation, spatial reasoning, and intelligent agent interaction capabilities at the 4B parameter scale. It supports long contexts and video understanding, and has powerful OCR and multi-language processing capabilities.

MultimodalTransformers

Qwen3-VL is the most powerful vision-language model in the Tongyi series. It has excellent text understanding and generation capabilities, in-depth visual perception and reasoning capabilities, long context support, strong spatial and video dynamic understanding capabilities, and outstanding agent interaction capabilities.

MultimodalGguf

Qwen3-VL-32B-Thinking is the most powerful vision-language model in the Qwen series, with excellent text understanding and generation capabilities, in-depth visual perception and reasoning capabilities, long context processing, spatial and video dynamic understanding capabilities, and outstanding agent interaction capabilities.

MultimodalTransformers

Qwen3-VL-8B-Thinking is the most powerful vision-language model in the Tongyi Qianwen series. It has excellent text understanding and generation capabilities, in-depth visual perception and reasoning capabilities, long context support, strong spatial and video dynamic understanding capabilities, and outstanding agent interaction capabilities.

MultimodalGguf

Qwen3-VL is the most powerful vision-language model in the Tongyi series. It has excellent text understanding and generation capabilities, in - depth visual perception and reasoning capabilities, long - context support, strong spatial and video dynamic understanding capabilities, and outstanding agent interaction capabilities.

MultimodalTransformers

Qwen3-VL is the most powerful vision - language model in the Tongyi series, with comprehensive upgrades in text understanding and generation, visual perception and reasoning, context length, spatial and video dynamic understanding, and agent interaction capabilities. This model offers both dense architecture and mixture - of - experts architecture, supporting flexible deployment from edge devices to the cloud.

MultimodalTransformers

Qwen3-VL is the most powerful vision-language model in the Qwen series, with excellent text understanding and generation capabilities, in-depth visual perception and reasoning capabilities, long context support, strong spatial and video dynamic understanding capabilities, and outstanding agent interaction capabilities.

MultimodalTransformers

Qwen3-VL is the most powerful vision-language model in the Qwen series, achieving comprehensive upgrades in all aspects, including excellent text understanding and generation capabilities, deeper visual perception and reasoning capabilities, longer context length, enhanced spatial and video dynamic understanding capabilities, and stronger agent interaction capabilities.

MultimodalTransformers

Qwen3-VL is a new-generation vision-language model launched by Alibaba, which has been comprehensively upgraded in text understanding, visual perception, spatial understanding, long context processing, and agent interaction, and supports flexible deployment from edge devices to the cloud.

MultimodalTransformers

This model is a violence detection model based on the VideoMAE architecture. After pre-training on the Kinetics dataset, it was fine-tuned for 92 rounds for the violence detection task. The model uses the Vision Transformer architecture and is specifically designed for video content analysis, capable of identifying violent behaviors in videos.

Computer VisionTransformers