2 citations · 4 across the 5 of their papers we have counts for
5 papers
Memory in Large Language Models: Mechanisms, Evaluation and Evolution
Dianxing Zhang, Wendong Li, Kani Song +4
Under a unified operational definition, we define LLM memory as a persistent state written during pretraining, finetuning, or inference that can later be addressed and that stably…
MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining
LLM-Core Xiaomi, :, Bingquan Xia +62
We present MiMo-7B, a large language model born for reasoning tasks, with optimization across both pre-training and post-training stages. During pre-training, we enhance the data p…
MiMo-VL Technical Report
Core Team, Zihao Yue, Zhenru Lin +71
We open-source MiMo-VL-7B-SFT and MiMo-VL-7B-RL, two powerful vision-language models delivering state-of-the-art performance in both general visual understanding and multimodal rea…
Beyond the EPICS: comprehensive Python IOC development with QueueIOC
Peng-Cheng Li, Xiao-Xue Bi, Ying-Ke Huang +6
Background and Purpose: Architectural deficiencies in EPICS lead to inefficiency in the development and application of EPICS IOCs. An unintrusive solution is replacing EPICS IOCs w…
Gear Training: A new way to implement high-performance model-parallel training
Hao Dong, Shuai Li, Dongchang Xu +2
The training of Deep Neural Networks usually needs tremendous computing resources. Therefore many deep models are trained in large cluster instead of single machine or GPU. Though…