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@VILA-Lab

VILA-Lab: Vision and Language Acceleration Lab

Vision/Language, learning and acceleration group

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  1. ATLAS ATLAS Public

    A principled instruction benchmark on formulating effective queries and prompts for large language models (LLMs). Our paper: https://arxiv.org/abs/2312.16171

    Python 960 98

  2. SRe2L SRe2L Public

    (NeurIPS 2023 spotlight) Large-scale Dataset Distillation/Condensation, 50 IPC (Images Per Class) achieves the highest 60.8% on original ImageNet-1K val set.

    Python 128 20

  3. Open-LLM-Leaderboard Open-LLM-Leaderboard Public

    Open-LLM-Leaderboard: Open-Style Question Evaluation. Paper at https://arxiv.org/abs/2406.07545

    Python 45 4

  4. M-Attack M-Attack Public

    A Simple Baseline Achieving Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1. Paper at: https://arxiv.org/abs/2503.10635

    Python 61 2

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