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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
  • 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
  • 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
  • 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
  • Mamba-3: Improved Sequence Modeling using State Space Principles
    This submission introduces Mamba-3, an “inference-first” state-space linear-time sequence model that aims to improve over prior sub-quadratic backbones (notably Mamba-2 and Gated DeltaNet) along three dimensions: modeling quality, state-tracking capability, and real-world decode efficiency The core methodological contributions are: Generalized trapezoidal discretization to improve
  • Bridging the Gap Between Promise and Performance for Microscaling. . .
    Experimental results on Llama-3 and Qwen models show that NVFP4 combined with MR-GPTQ recovers approximately 98–99% of FP16 accuracy, while MXFP4—despite its inherently larger quantization error—benefits substantially and approaches NVFP4-level performance
  • FlexPrefill: A Context-Aware Sparse Attention Mechanism for. . .
    TL;DR: FlexPrefill is a novel sparse attention mechanism for large language models that dynamically adapts attention patterns and computational budgets in real-time to optimize performance for each input and attention head
  • SAE as a Crystal Ball: Interpretable Features Predict Cross-domain . . .
    The Qwen-2 5-7B-Instruct model is trained on each dataset, and we evaluate: (1) the correlation between ICL feature drift and SFT, and (2) the correlation between actual performance shifts and our proposed metric
  • Shuai Bai - OpenReview
    Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, Jingren Zhou 19 Sept 2023 (modified: 10 Feb 2024) Submitted to ICLR 2024 M6-Fashion: High-Fidelity Multi-modal Image Generation and Editing
  • Towards Federated RLHF with Aggregated Client Preference for LLMs
    For example, our experiments demonstrate that the Qwen-2-0 5B selector provides strong performance enhancements to larger base models like Gemma-2B while ensuring computationally efficient This approach reduces the training burden for federated RLHF and broadens its applicability to resource-constrained scenarios





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