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

NVIDIA Nemotron Parse v1.1 TC is an advanced document semantic understanding model that can extract text and table elements with spatial positioning from images and generate structured annotations, including formatted text, bounding boxes, and semantic categories. Compared with the previous version, the speed is increased by 20%, and the page order of unordered elements is retained.

MultimodalTransformers

ERNIE-4.5-VL-28B-A3B-Thinking AWQ - INT8 is a multimodal large language model based on Baidu's ERNIE-4.5 architecture. It achieves 8-bit precision through the AWQ quantization technology, significantly reducing memory requirements while maintaining high performance. This model performs excellently in visual reasoning, STEM problem-solving, image analysis, etc., and has powerful multimodal understanding and reasoning abilities.

MultimodalTransformersMultiple Languages

Uni-MoE 2.0-Omni is a fully open-source full-modal model launched by Lychee Technology. It adopts the full-modal 3D RoPE and dynamic-capacity mixture-of-experts architecture, significantly enhancing the language-centered multi-modal understanding, reasoning, and generation capabilities. This version integrates full-modal understanding as well as audio and image generation capabilities.

MultimodalTransformersMultiple Languages

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

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

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

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

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

A quantized version of LightOnOCR-1B-1025, specifically designed for image-to-text tasks and widely used in fields such as document understanding and visual language processing. This model supports multiple European languages and is suitable for scenarios such as OCR, PDF processing, and table recognition.

MultimodalGgufMultiple Languages

DeepSeek-OCR-8bit is an MLX format version converted from the DeepSeek-OCR model. It is a visual language model specifically optimized for Apple chips, supporting multilingual OCR recognition and image text understanding tasks.

MultimodalMlxOther

This model is a vision-language model in MLX format converted from DeepSeek-OCR, specifically designed for optical character recognition (OCR) tasks. It supports multilingual text recognition and image understanding.

MultimodalMlxOther

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

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-32B-Thinking-AWQ is a quantized version based on the Qwen/Qwen3-VL-32B-Thinking model and is the most powerful vision-language model in the Tongyi series. This model has excellent text understanding and generation capabilities, in-depth visual perception and reasoning capabilities, supports long context and video understanding, and is suitable for image-text to text conversion tasks.

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

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

This is the 4-bit quantized version of the Qwen3-VL-4B-Instruct model, specifically optimized for Apple Silicon chips and converted using the MLX framework. This model is a vision-language model that supports image understanding and multimodal dialogue tasks.

MultimodalMlx

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