JanusCoder-14B is an open-source foundation model with 14B parameters built on Qwen3-14B, aiming to establish a unified visual programming interface for code intelligence. This model is trained on the JANUSCODE-800K multimodal code corpus and can handle various visual programming tasks in a unified manner.

Natural Language ProcessingTransformers

JanusCoder-8B is an open-source code intelligence foundation model built on Qwen3-8B, aiming to establish a unified visual programming interface. This model is trained on JANUSCODE-800K (the largest multimodal code corpus to date) and can handle various visual programming tasks, including data visualization, interactive web UI, and code-driven animation.

MultimodalTransformers

Qwen3-VL-2B-Instruct is the most powerful vision-language model in the Qwen series. It has excellent text understanding and generation capabilities, in-depth visual perception and reasoning abilities, long context support, and strong spatial and video dynamic understanding abilities. This model uses a 2B parameter scale, supports instruction interaction, and is suitable for multimodal AI applications.

MultimodalTransformers

This project provides a quantized version of the Huihui Qwen3-VL-30B-A3B-Instruct model, aiming to improve the performance and efficiency of the model in specific scenarios. This is a visual language model based on the Qwen3-VL architecture, supporting multimodal interaction between images and text.

MultimodalGguf

LFM2-VL-3B is a multimodal vision-language model developed by Liquid AI, built on the LFM2 backbone architecture. It has powerful visual understanding and reasoning capabilities, especially excelling in fine-grained perception tasks. This model can efficiently process text and image inputs and supports native image processing with a resolution of up to 512×512.

MultimodalTransformersMultiple Languages

Bee-8B is an advanced fully open-source multimodal large language model that focuses on data quality and aims to narrow the performance gap with proprietary models. By using the high-quality Honey-Data-15M corpus and the advanced data processing pipeline HoneyPipe, it demonstrates excellent performance in complex reasoning and other aspects.

MultimodalTransformersEnglish

Apriel-1.5-15b-Thinker is a multimodal reasoning model developed by the ServiceNow SLAM Lab, with a parameter scale of 15 billion. It has powerful text and image understanding and reasoning capabilities, adopting a chained reasoning mode of 'think-answer', aiming to achieve performance comparable to large models with a smaller model scale.

MultimodalTransformers

NVIDIA Qwen2.5-VL-7B-Instruct-FP4 is a quantized version of Alibaba's Qwen2.5-VL-7B-Instruct model. It adopts an optimized Transformer architecture, supports multimodal inputs (text and images), and is suitable for various AI application scenarios. This model undergoes FP4 quantization through the TensorRT Model Optimizer, providing efficient inference performance on NVIDIA GPUs.

MultimodalSafetensors

GLM-4.5V-AWQ-4bit is a quantized multimodal model built on Zhipu AI's next-generation flagship text foundation model. Optimized by AWQ-4bit quantization technology, it significantly reduces the computational resource requirements while maintaining excellent performance. This model achieves SOTA performance among models of the same scale in 42 public visual language benchmark tests and has powerful visual reasoning capabilities.

MultimodalTransformersMultiple Languages

GLM-4.1V-9B-Base is an open-source vision-language foundation model developed by Zhipu AI. It has 9 billion parameters, focuses on multimodal reasoning capabilities, supports both Chinese and English, and can process images with up to 4K resolution and a context length of 64K.

MultimodalTransformersMultiple Languages

MedGemma-4B-IT is a medical multimodal model based on Gemma 3, supporting the understanding of medical text and images, and suitable for the development of medical AI applications.

MultimodalTransformers

Cosmos-Reason1 is a physical artificial intelligence model developed by NVIDIA. It has the ability to understand physical common sense and can generate embodied decisions through long-chain thinking reasoning. This model supports multimodal input (text + video/image) and outputs text, which is suitable for physical AI fields such as robotics and autonomous driving.

MultimodalTransformersEnglish

MedGemma is a medical multimodal model developed based on Gemma 3, focusing on medical text and image understanding and supporting the construction of healthcare AI applications.

MultimodalTransformers

MedGemma is a series of AI models developed by Google, specifically optimized for medical scenarios. It includes two versions: a 4B multimodal version and a 27B pure-text version. Based on the Gemma 3 architecture, it performs excellently in medical text and image understanding tasks and can effectively support the development of medical AI applications.

MultimodalTransformers

MedGemma is a medical-specific multimodal AI model developed by Google, based on the Gemma 3 architecture, focusing on medical text and image understanding.

MultimodalTransformers

Llama-4-Scout is one of the Llama 4 series models launched by Meta. It adopts a Mixture of Experts (MoE) architecture and is a native multimodal AI model that supports text and image input. It performs excellently in multilingual text understanding and visual tasks. This model has 17B parameters and 16 experts and is designed for commercial and research purposes.

MultimodalSafetensorsMultiple Languages

Gemma3-R1984-4B is a powerful agent AI platform built upon Google's Gemma-3-4B model, supporting multimodal file processing and deep research capabilities.

MultimodalTransformersMultiple Languages

The Llama 4 series is a native multimodal AI model launched by Meta. It adopts the Mixture of Experts architecture, supports text and image interaction, and performs excellently in various language and visual tasks.

MultimodalTransformersMultiple Languages

The Llama 4 series is a native multimodal AI model launched by Meta, supporting text and image interaction. It adopts the Mixture of Experts architecture and performs excellently in text and image understanding.

MultimodalTransformersMultiple Languages

Llama 4 Scout is a native multimodal AI model launched by Meta, supporting multilingual text and image understanding. It adopts the Mixture of Experts architecture and has industry-leading performance in text and image understanding.

MultimodalTransformersMultiple Languages