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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 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 952 96

  2. 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 125 19

  3. Open-LLM-Leaderboard Public

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

    Python 43 4

  4. 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 35 2

Repositories

Showing 10 of 11 repositories
  • M-Attack-Website Public
    JavaScript 0 0 0 0 Updated Mar 20, 2025
  • 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 35 MIT 2 0 2 Updated Mar 19, 2025
  • DELT Public

    (CVPR 2025) Official implementation to DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation which outperforms SOTA top 1-acc by +1.3% and increases diversity per class by +5%

    Python 20 0 0 0 Updated Feb 27, 2025
  • Mobile-MMLU Public

    Mobile-MMLU: A Mobile Intelligence Language Understanding Benchmark

    Python 8 MIT 1 0 0 Updated Dec 27, 2024
  • Mobile_MMLU Public
    HTML 0 0 0 0 Updated Dec 25, 2024
  • 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 125 19 1 0 Updated Nov 15, 2024
  • Open-LLM-Leaderboard Public

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

    Python 43 CC-BY-4.0 4 1 0 Updated Jun 27, 2024
  • Open-LLM-Leaderboard-Website Public
    HTML 1 0 0 0 Updated Jun 15, 2024
  • 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 952 Apache-2.0 96 6 0 Updated May 28, 2024
  • GBLM-Pruner Public

    Are gradient information useful for pruning of LLMs?

    Python 43 MIT 8 1 0 Updated Apr 22, 2024
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