VILA-Lab/ATLASPython
A principled instruction benchmark on formulating effective queries and prompts for large language models (LLMs). Our paper: https://arxiv.org/abs/2312.16171
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倉庫
A principled instruction benchmark on formulating effective queries and prompts for large language models (LLMs). Our paper: https://arxiv.org/abs/2312.16171
A Systematic Analysis and Discussion of Claude Code for Designing Today's and Future AI Agent Systems
Are gradient information useful for pruning of LLMs?
(NeurIPS 2023 spotlight) Large-scale Dataset Distillation/Condensation, 50 IPC (Images Per Class) achieves the highest 60.8% on original ImageNet-1K val set.