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

OpenMMReasoner is a completely transparent two-stage multimodal reasoning solution, trained using supervised fine-tuning (SFT) and reinforcement learning (RL). A cold-start dataset of 874K samples was constructed in the SFT stage, and the RL stage used 74K samples to further enhance its capabilities, showing excellent performance in multimodal reasoning benchmark tests.

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

SenseNova-SI is a series of spatial intelligence enhancement models built on multimodal foundation models. Through careful training with 8 million samples of data, it has achieved excellent performance in multiple spatial intelligence benchmark tests while maintaining strong general multimodal understanding capabilities.

MultimodalTransformersEnglish

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

SenseNova-SI is a series of spatial intelligence models built on multimodal foundation models, specifically optimized for spatial understanding capabilities. Through large-scale spatial intelligence data training, it performs excellently in metric estimation, spatial relationship understanding, viewpoint change handling, etc.

MultimodalTransformers

Qwen3-VL-2B-Thinking is one of the most powerful vision-language models in the Qwen series. It uses GGUF format weights and supports efficient inference on devices such as CPUs, NVIDIA GPUs, and Apple Silicon. This model has excellent multimodal understanding and reasoning capabilities, especially enhancing visual perception, spatial understanding, and agent interaction functions.

MultimodalTransformers

Qwen3-VL-8B-Thinking is the most powerful vision-language model in the Tongyi Qianwen series, with an 8B parameter version featuring enhanced reasoning capabilities. This model has been comprehensively upgraded in text understanding, visual perception, spatial understanding, long context processing, etc., and supports multimodal reasoning and agent interaction.

MultimodalGguf

Qwen3-VL-4B-Instruct is one of the most powerful vision-language models in the Tongyi series. It has been comprehensively upgraded in text understanding, visual perception, spatial understanding, video processing, etc. It supports running on a variety of hardware devices and has excellent multimodal reasoning capabilities.

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-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

Next 12B is a multimodal vision-language model with 12 billion parameters based on Gemma 3. It is the most advanced open-source vision-language model in Turkey. This model performs excellently in text and image understanding, with advanced inference and context-aware multimodal output capabilities. It specifically provides professional-level Turkish language support and also has extensive multilingual capabilities.

MultimodalTransformersMultiple Languages

This is a 24B parameter efficient inference model built on Mistral Small 3.2 and converted to MLX-MXFP4 format. The model has enhanced inference capabilities, supports multimodal input, has a 128k context window, and can run on an RTX 4090 or a MacBook with 32GB RAM.

MultimodalMlxMultiple Languages

This is the MXFP4_MOE quantized version of the Qwen3-VL-235B-A22B-Thinking model, specifically designed for image-text to text conversion tasks. The model is quantized based on the 1M context version extended by unsloth, maintaining strong multimodal understanding capabilities.

MultimodalGguf

NVIDIA-Nemotron-Nano-VL-12B-V2-FP8 is a quantized vision-language model launched by NVIDIA, which adopts an optimized Transformer architecture and has undergone three-stage training on commercial images. This model supports single-image inference, has multilingual and multimodal processing capabilities, and is suitable for various scenarios such as image summarization and text-image analysis.

MultimodalTransformers

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

NVIDIA Nemotron Nano v2 12B VL is a powerful multimodal vision-language model that supports multi-image reasoning and video understanding, and has document intelligence, visual question answering, and summarization capabilities. It can be used for commercial purposes.

MultimodalTransformers

Qwen3-VL-32B-Instruct is a powerful multimodal vision-language model developed based on the Qwen foundation model. After optimization and quantization, it is specifically optimized for Apple chip devices, providing efficient image-text processing 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 understanding capabilities, etc., and it has excellent multimodal interaction capabilities.

MultimodalTransformers