Qwen3-VL is the most powerful vision-language model in the Tongyi large model 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 intelligent agent interaction capabilities. This model uses a Mixture of Experts (MoE) architecture and is a thinking version for enhanced reasoning.

MultimodalGguf

AesCoder-4B is a large language model focused on improving the aesthetic quality of code. It optimizes the aesthetic performance of code generation through an intelligent reward feedback mechanism and performs well in visual coding tasks such as web design and game development.

Natural Language ProcessingTransformers

Qwen3-VL is the most powerful vision-language model in the Tongyi large model 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.

MultimodalSafetensors

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

Ming-UniVision is a large multimodal language model that first integrates continuous visual representations into the next-token prediction framework, unifying vision and language under a single autoregressive paradigm without discrete quantization or modality-specific heads. This model supports joint image understanding and generation, converges faster in vision-language training, and also supports multi-round context visual tasks.

MultimodalTransformersMultiple Languages

Lingshu is a SOTA multimodal large language model in the medical field, performing excellently in medical visual Q&A and report generation tasks.

MultimodalTransformers

Llama 3.2-Vision is a multimodal large language model developed by Meta, with image reasoning and text generation capabilities, supporting tasks such as visual recognition, image description, and question answering.

MultimodalTransformersMultiple Languages

VARGPT-v1.1 is a visual autoregressive unified large model, enhanced through iterative instruction tuning and reinforcement learning, capable of performing both visual understanding and generation tasks.

MultimodalTransformersEnglish

LLaVA-Gemma-7b is a large multimodal model trained based on the LLaVA-v1.5 framework, using google/gemma-7b-it as the language backbone combined with a CLIP visual encoder, suitable for multimodal understanding and generation tasks.

MultimodalTransformersEnglish

GLaMM-FullScope is a multimodal large model that integrates all capabilities of GLaMM, including scene dialogue generation, referring expression segmentation, region-level image description, image-level description generation, and visual question answering.

MultimodalTransformers

BLIP-2 is a vision-language model that combines an image encoder with a large language model for image-to-text generation and visual question answering tasks.

MultimodalTransformersEnglish

BLIP-2 is a vision-language model based on Flan T5-xxl, pretrained by freezing the image encoder and large language model, supporting tasks like image caption generation and visual question answering.

MultimodalTransformersEnglish

BLIP-2 is a vision-language model that achieves language-image pretraining by freezing the image encoder and large language model, supporting tasks such as image caption generation and visual question answering.

MultimodalTransformersEnglish

BLIP-2 is a vision-language model that combines an image encoder with a large language model for image-to-text generation and visual question answering tasks.

MultimodalTransformersEnglish

BLIP-2 is a vision-language model based on OPT-6.7b, pretrained by freezing the image encoder and large language model, supporting tasks such as image-to-text generation and visual question answering.

MultimodalTransformersEnglish

GIT is a Transformer-based image-to-text generation model trained on large-scale image-text pairs, capable of performing tasks such as image captioning and visual question answering.

MultimodalTransformersMultiple Languages