Wan2.2 is a major upgrade version of the basic video model, focusing on integrating innovative technologies such as effective MoE architecture, efficient training strategies, and multimodal fusion into the video diffusion model, bringing more powerful and efficient solutions to the field of video generation.

MultimodalDiffusersMultiple Languages

Qwen3-VL-4B-Instruct is a 4-billion-parameter visual language model launched by Alibaba. It is developed based on the Qwen3 architecture and supports multimodal understanding and dialogue tasks. This model has powerful image understanding and text generation capabilities, and can handle complex visual language interaction scenarios.

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

nutrient-gram-qwen-3-vl-2b is a fine-tuned version based on Qwen/Qwen3-VL-2B-Instruct, trained using TRL for supervised fine-tuning, mainly used for text generation tasks. This model is optimized for specific tasks while maintaining its original multimodal capabilities.

Natural Language ProcessingTransformers

This is a GGUF format version converted from the Qwen3-VL-30B-A3B-Instruct model, specifically optimized for llama.cpp. This model is a multimodal visual language model with 30 billion parameters, supporting image understanding and text generation tasks.

MultimodalGguf

Qwen3-VL-2B-Thinking is a vision-language model launched by Qwen. Based on a 2B parameter scale, it uses MLX for 8-bit quantization and is specifically optimized for Apple Silicon chips. This model supports multimodal understanding and generation tasks for images and text.

MultimodalTransformers

Emu3.5 is a native multimodal model developed by the Beijing Academy of Artificial Intelligence (BAAI). It can jointly predict the next state across vision and language, enabling coherent world modeling and generation. After end-to-end pre-training and large-scale reinforcement learning post-training, it demonstrates excellent performance in multimodal tasks.

MultimodalTransformers

Emu3.5 is a native multimodal model developed by the Beijing Academy of Artificial Intelligence (BAAI). It can jointly predict the next state across vision and language, achieving coherent world modeling and generation, and performs excellently in multimodal tasks.

MultimodalTransformers

Qwen3-VL-2B-Instruct-GGUF is the GGUF quantized version of the multimodal vision-language model in the Tongyi Qianwen series. It has 2 billion parameters and supports the seamless integration of image understanding and text generation. It can run efficiently on devices such as CPUs and GPUs.

MultimodalTransformers

Qwen3-VL is the most powerful vision-language model in the Qwen series to date. It has been comprehensively upgraded in text understanding and generation, visual perception and reasoning, context length, spatial and video dynamic understanding, and agent interaction capabilities. This model adopts a Mixture of Experts (MoE) architecture, providing excellent multimodal processing capabilities.

MultimodalGguf

Qwen3-VL is the latest generation of vision-language model launched by Alibaba, with significant improvements in text understanding, visual perception, spatial understanding, video analysis, and agent interaction. This model supports multimodal input and has powerful reasoning ability and long context processing ability.

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

The MLX MXFP4 quantized version of Gemma 3 27B IT QAT is a lightweight open - source multimodal model developed by Google. This model can simultaneously process text and image inputs and generate text outputs. It has a large context window of 128K and supports over 140 languages, making it suitable for various text generation and image understanding tasks.

MultimodalMlx

This is a repaired image text-to-text model that solves the problem of inference failure of the original model in the latest Transformers version. This model is specifically designed for image recognition and text generation tasks and supports multimodal input processing.

MultimodalTransformersEnglish

Qwen3-VL-30B-A3B-Instruct is a multimodal vision-language model based on the Qwen3-VL-30B model, which supports joint understanding and generation tasks of images and text. This model adopts an advanced vision-language fusion architecture and can handle complex multimodal reasoning tasks.

MultimodalGguf

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 understanding capabilities, etc., and it has excellent multimodal interaction capabilities.

MultimodalTransformers

DreamOmni2-GGUF is an image-to-image model that converts the xiabs/DreamOmni2-7.6B model to the GGUF format. It supports multimodal instruction editing and generation and follows the Apache-2.0 license.

MultimodalGguf

This is the 8-bit quantized version of the Qwen3-VL-4B-Instruct model in MLX format, converted by mlx-community. This model is a multimodal vision-language model with 4 billion parameters, supporting image understanding and text generation tasks, and optimized for instruction following scenarios.

MultimodalMlx

The Ming-flash-omni preview version is a multimodal large model built on the Ling-Flash-2.0 sparse mixture of experts (MoE) architecture, with a total of 100B parameters and only 6B parameters activated per token. This model is a comprehensive upgrade based on Ming-Omni, showing significant improvements in multimodal understanding and generation, especially in speech recognition, image generation, and segmentation editing.

MultimodalDiffusersEnglish

Qwen3-VL-8B-Thinking is an 8-billion-parameter multimodal large language model developed by the Qwen team at Alibaba Cloud. It is designed for deep multimodal reasoning, supporting visual understanding, long context processing, and structured chain-of-thought generation, and performs excellently in complex reasoning tasks.

MultimodalMlx