5 papers
HexiScale: Facilitating Large Language Model Training over Heterogeneous Hardware
Ran Yan, Youhe Jiang, Xiaonan Nie +3
Training large language models (LLMs) is a computationally intensive task, which is typically conducted in data centers with homogeneous high-performance GPUs. In this paper, we ex…
TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation
Lin Sun, Guangxiang Zhao, Xiaoqi Jian +18
The challenge of reducing the size of Large Language Models (LLMs) while maintaining their performance has gained significant attention. However, existing methods, such as model di…
Learning from History: A Retrieval-Augmented Framework for Spatiotemporal Prediction
Hao Jia, Penghao Zhao, Hao Wu +3
Accurate and long-term spatiotemporal prediction for complex physical systems remains a fundamental challenge in scientific computing. While deep learning models, as powerful param…
PQCache: Product Quantization-based KVCache for Long Context LLM Inference
Hailin Zhang, Xiaodong Ji, Yilin Chen +5
As the field of Large Language Models (LLMs) continues to evolve, the context length in inference is steadily growing. Key-Value Cache (KVCache), the intermediate representations o…
MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training
Pinxue Zhao, Hailin Zhang, Fangcheng Fu +9
Nowadays, Large Language Models (LLMs) have been trained using extended context lengths to foster more creative applications. However, long context training poses great challenges…