- Qwen-VL: A Versatile Vision-Language Model for Understanding . . .
In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models (LVLMs) designed to perceive and understand both texts and images Starting from the Qwen-LM as a
- Gated Attention for Large Language Models: Non-linearity, Sparsity,. . .
The authors response that they will add experiments in QWen architecture, give the hyperparameters, and promise to open-source one of the models Reviewer bMKL is the only reviewer to initially score the paper in the negative region (Borderline reject) They have some doubts on the experimental section
- Q -VL: A VERSATILE V M FOR UNDERSTANDING, L ING AND EYOND QWEN-VL: A . . .
In this paper, we explore a way out and present the newest members of the open-sourced Qwen fam-ilies: Qwen-VL series Qwen-VLs are a series of highly performant and versatile vision-language foundation models based on Qwen-7B (Qwen, 2023) language model We empower the LLM base-ment with visual capacity by introducing a new visual receptor including a language-aligned visual encoder and a
- LLaVA-MoD: Making LLaVA Tiny via MoE-Knowledge Distillation
LLaVA-MoD introduces a framework for creating efficient small-scale multimodal language models through knowledge distillation from larger models The approach tackles two key challenges: optimizing network structure through sparse Mixture of Experts (MoE) architecture, and implementing a progressive knowledge transfer strategy This strategy combines mimic distillation, which transfers general
- 多模态大语言模型综述 - OpenReview
摘 要在过去的一年里,多模态大语言模型(Multimodal Large Language Models, MM-LLMs)取得了显著进展,通过经济高效的训练策略,增强了现成的LLMs 对多模态输入或输出的支持。这些模型不仅保留了LLMs固有的推理和决策能力,还增强了对各种多模态任务的处理能力。本文提供了一份全面的调查,旨在促进多模态大型
- Junyang Lin - OpenReview
Junyang Lin Principal Researcher, Qwen Team, Alibaba Group Joined July 2019
- Chujie Zheng - OpenReview
Chujie Zheng Researcher, Qwen Team, Alibaba Group Joined April 2021
- Variational Reasoning for Language Models | OpenReview
We empirically validate our method on the Qwen 2 5 and Qwen 3 model families across a wide range of reasoning tasks Overall, our work provides a principled probabilistic perspective that unifies variational inference with RL-style methods and yields stable objectives for improving the reasoning ability of language models
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