5 papers
FD: A Dedicated Framework for Fine-Grained Dataset Distillation
Hongxu Ma, Guang Li, Shijie Wang +5
Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong results on general benchmarks. Decoup…
Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology
Jucheng Hu, Zhangquan Chen, Yulin Chen +9
Deciphering animal intent is a fundamental challenge in computational ethology, largely because of semantic aliasing, the phenomenon where identical external signals (e.g., a cat's…
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augmentation
Jucheng Hu, Suorong Yang, Dongzhan Zhou
Visual Instruction Finetuning (VIF) is pivotal for post-training Vision-Language Models (VLMs). Unlike unimodal instruction finetuning in plain-text large language models, which ma…
DONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning
Jucheng Hu, Surong Yang, Lijun Wu +1
Ad-hoc instruction fine-tuning of large language models (LLMs) is widely adopted for domain-specific adaptation. While domain-specific supervised fine-tuning (SFT) is effective and…