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

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 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

This is the Llamacpp imatrix quantized version of the Qwen3-VL-2B-Thinking model, providing files of various quantization types that can run efficiently in different hardware environments. This model is a multimodal vision-language model with 2B parameters and has the ability of chain-of-thought reasoning.

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

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

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

This is a 4-bit quantized version converted from the Qwen3-VL-32B-Thinking model, specifically optimized for the MLX framework. This model is a multimodal vision-language model with a parameter scale of 32B. It has the ability of chain-of-thought reasoning and can process both image and text inputs simultaneously to generate high-quality text responses.

MultimodalTransformers

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 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

Qwen3-VL-30B-A3B-Instruct-AWQ is a quantized version of the Qwen3-VL series, which has powerful visual language processing capabilities and supports tasks such as image understanding, video analysis, and multimodal reasoning. The model has significant improvements in text understanding, visual perception, spatial understanding, and long context processing.

MultimodalTransformers

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

Qwen3-VL-4B-Instruct is a 4-billion-parameter instruction-tuned multimodal large language model launched by the Alibaba Cloud Qwen team. It is optimized for Qualcomm NPUs, integrating powerful visual-language understanding capabilities with dialogue fine-tuning functions, and is suitable for practical application scenarios such as chat reasoning, document analysis, and visual dialogue.

MultimodalGguf

The Q4_K_S quantized GGUF version of the Qwen3-Omni-30B-A3B-Thinking model, supporting multimodal processing of text, vision, and audio, and achieving efficient reasoning through llama.cpp. This version significantly reduces the file size and improves the reasoning speed while ensuring quality.

MultimodalGgufMultiple 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 15-billion parameter multimodal reasoning model developed by ServiceNow. It has the ability to reason with text and images, and its performance can rival models 10 times larger. It scored 52 points on the manual analysis index and performed excellently in enterprise domain benchmark tests.

MultimodalTransformers

Apriel-1.5-15B-Thinker is a 15-billion-parameter multimodal model designed for image understanding and reasoning. It uses the mid-term training method instead of RLHF training. This version is an MLX quantized version suitable for Apple chips, featuring low memory usage and fast startup speed.

MultimodalMlxEnglish