This project provides a quantized version (Int4) of Qwen/Qwen3-VL-32B-Thinking, aiming to lower the hardware threshold for model deployment and make the knowledge of advanced visual language models accessible to everyone.

MultimodalGguf

ActIO-UI-7B-RLVR is a 7-billion-parameter visual language model released by Uniphore, specifically designed for computer interface automation tasks. It is based on Qwen2.5-VL-7B-Instruct and optimized through supervised fine-tuning and reinforcement learning with verifiable rewards. It performs excellently in tasks such as GUI navigation, element positioning, and interaction planning, and has achieved the leading level among open-source 7B models in the WARC-Bench benchmark test.

MultimodalTransformersEnglish

NewBie image Exp0.1 is an efficient basic image generation model developed based on the Next-DiT architecture, specifically designed for generating high-quality anime-style images. It integrates advanced text encoders and visual components, supports natural language and structured tag input, and is a powerful tool for multi-character anime image generation.

Computer VisionDiffusersEnglish

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

Jan-v2-VL is a vision-language model with 8 billion parameters, designed specifically for long-term, multi-step tasks in real software environments (such as browsers and desktop applications). It combines language reasoning with visual perception, can follow complex instructions, maintain intermediate states, and recover from minor execution errors.

MultimodalGguf

Jan-v2-VL is a vision-language model with 8 billion parameters, designed specifically for executing long-term, multi-step tasks in real software environments such as browsers and desktop applications. It tightly integrates language reasoning with visual perception, can follow complex instructions, maintain intermediate states, and recover from minor execution errors to achieve stable and continuous task completion.

MultimodalTransformersEnglish

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

This is a quantized version model based on Moonshot AI technology, focusing on visual language understanding and generation tasks, and committed to lowering the threshold of knowledge acquisition and making knowledge accessible to everyone.

Natural Language ProcessingGguf

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 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 dynamic understanding, and agent interaction capabilities. This model offers both dense architecture and mixture - of - experts architecture, supporting flexible deployment from edge devices to the cloud.

MultimodalTransformers

Qwen3-VL is the most powerful visual language model in the Tongyi series. It has excellent text understanding and generation capabilities, in - depth visual perception and reasoning abilities, long - context support, strong spatial and video dynamic understanding abilities, and agent interaction capabilities. This repository provides GGUF - format weights, supporting efficient inference on devices such as CPUs and GPUs.

MultimodalTransformers

Qwen3-VL-8B-Instruct is the most powerful visual language model in the Tongyi series, with excellent text understanding and generation capabilities, in-depth visual perception and reasoning abilities, long context support, and strong spatial and video dynamic understanding abilities.

MultimodalTransformers

Qwen3-VL is the most powerful vision - language model in the Tongyi series. It has been comprehensively upgraded in aspects such as text understanding and generation, visual perception and reasoning, context length, spatial and video dynamic understanding, and agent interaction capabilities. This model offers both dense architecture and mixture - of - experts architecture, supporting flexible deployment from edge devices to the cloud.

MultimodalGguf

Qwen3 - VL is the most powerful vision - language model in the Tongyi series. It has excellent text understanding and generation capabilities, in - depth visual perception and reasoning abilities, long - context support, strong spatial and video dynamic understanding abilities, and outstanding intelligent agent interaction abilities.

MultimodalTransformers

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 is a multimodal model that supports multilingual processing, covering multiple fields such as natural language processing, code processing, and audio processing. It can perform various tasks such as automatic speech recognition, speech summarization, speech translation, and visual question answering.

MultimodalSafetensorsMultiple Languages

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

This project provides a quantized version of the Huihui Qwen3-VL-30B-A3B-Instruct model, aiming to improve the performance and efficiency of the model in specific scenarios. This is a visual language model based on the Qwen3-VL architecture, supporting multimodal interaction between images and text.

MultimodalGguf

Qwen3-VL-2B-Instruct is an efficient image-text-to-text model developed by the Qwen team. This model is optimized with MLX 8-bit quantization and is particularly suitable for Apple Silicon devices. It can handle visual language tasks and provide efficient solutions.

MultimodalTransformers