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- 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
- TwinFlow: Realizing One-step Generation on Large Models with. . .
Qwen-Image-Lightning is 1 step leader on the DPG benchmark and should be marked like this in Table 2 Distillation Fine Tuning vs Full training method: Qwen-Image-TwinFlow (and possibly also TwinFlow-0 6B and TwinFlow-1 6B, see question below) leverages a pretrained model that is fine-tuned
- Function-to-Style Guidance of LLMs for Code Translation
By adopting a Hybrid Mining strategy—using Qwen LLMs for C, C++, and Java, and DeepSeek LLMs for Go and Python—we achieved consistent performance improvements This demonstrates that assigning tasks according to each model's strengths can alleviate the impact of LLMs’ inherent biases and improve the quality of training data
- AgentFold: Long-Horizon Web Agents with Proactive Context Folding
LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by a fundamental trade-off in context management Prevailing ReAct-based
- 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
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