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

This is a 🤗 Transformers model published on the Hugging Face Hub, and its model card is automatically generated by the system. Currently, most detailed information in the card (such as developers, model type, language, license, etc.) is pending completion.

Natural Language ProcessingTransformers

This is a LoRA fine-tuned model based on Qwen-Image-Edit-2509, specifically designed to solve the problem of secondary illumination processing for images. It can remove the illumination effect of the original image and regenerate realistic lighting and shadows for the target image according to the illumination and hue of the reference image, achieving the migration and fusion of image styles.

Computer VisionDiffusers

CodeV is a 7-billion-parameter vision-language model fine-tuned based on Qwen2.5-VL-7B-Instruct. Through two-stage training of supervised fine-tuning (SFT) and reinforcement learning (RL) based on tool-aware policy optimization (TAPO), it aims to achieve reliable and interpretable visual reasoning. It represents visual tools as executable Python code and ensures the consistency between tool usage and question evidence through a reward mechanism, solving the problem of irrelevant tool invocation under high accuracy.

MultimodalTransformersMultiple Languages

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

OpenThinker-Agent-v1-SFT is an agent model obtained through supervised fine-tuning (SFT) based on Qwen/Qwen3-8B. It is the first-stage model of the complete training process (SFT + RL) of OpenThinker-Agent-v1, specifically optimized for agent tasks (such as terminal operations and code repair).

Natural Language ProcessingTransformersOther

OpenThinker - Agent - v1 is an open - source intelligent agent model that is subsequently trained based on Qwen3 - 8B. It is designed for terminal operations and software engineering tasks. It is first trained on a high - quality supervised fine - tuning dataset and then further optimized through reinforcement learning. It performs excellently in agent benchmark tests such as Terminal - Bench 2.0 and SWE - Bench.

Natural Language ProcessingTransformersOther

Qwen3-4B-Hivemind-Instruct-NEO-MAX-Imatrix-GGUF is a large language model with 4B parameters for instruction fine-tuning based on the Qwen3 architecture. It uses NEO Imatrix and MAX quantization technologies and has the ability to process an ultra-long context of 256k. This model performs excellently in multiple benchmark tests and is a powerful general-purpose model.

Natural Language ProcessingGgufEnglish

Qwen-Image is a text-to-image generation model developed based on the Qwen series. It supports both Chinese and English inputs and performs excellently on multiple evaluation metrics. It is particularly suitable for image generation scenarios that pursue realistic effects.

MultimodalDiffusersMultiple Languages

This is the quantized version of Qwen3-VL-32B-Thinking-heretic, using a new ablation method for quantization. Compared with traditional methods, it causes less damage to the base model. It offers multiple quantization precision versions and is suitable for running on devices with 24GB of VRAM, supporting image-to-text tasks.

MultimodalTransformersEnglish

This is a hybrid - language AI model specifically designed for call record analysis, capable of handling call transcription content in Hindi, English, and mixed Hindi - English. The model is fine - tuned based on Qwen2.5 - 1.5B - Instruct and has powerful multilingual understanding and information extraction capabilities.

Natural Language ProcessingTransformersMultiple Languages

TomoroAI/tomoro-colqwen3-embed-4b is an advanced ColPali-style multimodal embedding model that can map text queries, visual documents (such as images, PDFs) or short videos into aligned multi-vector embeddings. This model combines the advantages of Qwen3-VL-4B-Instruct and Qwen3-Embedding-4B, performs excellently in the ViDoRe benchmark test, and significantly reduces the embedding space occupation.

MultimodalTransformersOther

This model is obtained through distillation training using the high-reasoning-difficulty Gemini 3 Pro preview dataset based on the Qwen3-4B-Thinking-2507 base model. It focuses on improving complex reasoning abilities in the fields of coding and science. Through training with specific datasets, it aims to efficiently transfer the reasoning abilities of large models (such as Gemini 3 Pro) to a smaller-scale model.

Natural Language ProcessingTransformers

Qwen3 VL 4B Thinking is a multimodal model that supports image-to-text and text-to-text conversion. It has a parameter scale of 4B and can meet various image-text interaction needs.

MultimodalGguf

This is a version of the Qwen3-Next-80B large language model after 4-bit quantization using Huawei's SINQ (Sinkhorn Normalized Quantization) method. SINQ is a novel, fast, and high-quality quantization method designed to significantly compress the model size (by approximately 75%) while maintaining almost the same accuracy as the original model, making it easier to deploy.

Natural Language ProcessingSafetensorsEnglish

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

TweeTaal-nl-en-0.6B is a language model fine-tuned specifically for Dutch-English bidirectional translation tasks, developed based on the Qwen3-0.6B architecture. This model can still provide accurate and fluent translation services in resource-constrained environments, supporting mutual translation between Dutch and English.

Natural Language ProcessingTransformersOther

This is an open-source LoRA model specifically designed for Qwen-Image, focusing on simulating the realistic look and feel of modern iPhone photography. The model is trained on more than 5000 real iPhone-style photos and can generate clear, natural images suitable for social media sharing.

Computer VisionDiffusers

Fyodor-Q3-8B-Instruct is a high-fidelity instruction-tuned model designed for intelligent reasoning and robust code generation. It is based on the Qwen3-8B architecture and trained with a high-rank LoRA configuration. It excels in planning before coding and is well-suited for complex software engineering tasks.

Natural Language ProcessingTransformersMultiple Languages

This is a LoRA model based on Qwen Image Edit 2509, specifically designed to convert input images into sketch comic artworks with exaggerated features. The model can create humorous and artistic comic images for human and animal subjects, highlighting facial features and characteristics.

Computer VisionDiffusersMultiple Languages