Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date. This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.
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View details ↗Website • Learn more • GGUF • Enterprise licensing Swift-Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B, using **58.3% fewer thinking tokens** while maintaining near-identical performance (**<1% loss**) and as a result getting a **x1.95 speed-up** on several tasks. We built Swift by identifying reasoning-marker tokens that, in our analysis, trigger overthinking in Qwen’s reasoning rollouts. We then fine-tuned Qwen by penalizing usage of those tokens while it reasons.
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View details ↗This repository contains the code and model weights for the matting module in [LayerD [ICCV'25]](https://arxiv.org/abs/2509.25134), a layer decomposition framework for graphic design images. The model in this repository is **intended to be used as a part of the original LayerD github repository**. Please visit https://github.com/CyberAgentAILab/LayerD for more information. The model architecture code is based on the BiRefNet repository. We thank the authors for releasing their high-quality matting model. This repository is intended for use with LayerD, so we recommend following the instructions in the LayerD repository.
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