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Fairy2i: Training Complex LLMs from Real LLMs with All Parameters in
Feiyu Wang, Xinyu Tan, Bokai Huang +4
Large language models (LLMs) have revolutionized artificial intelligence, yet their massive memory and computational demands necessitate aggressive quantization, increasingly pushi…
Rank Also Matters: Hierarchical Configuration for Mixture of Adapter Experts in LLM Fine-Tuning
Peizhuang Cong, Wenpu Liu, Wenhan Yu +2
Large language models (LLMs) have demonstrated remarkable success across various tasks, accompanied by a continuous increase in their parameter size. Parameter-efficient fine-tunin…
BATON: Enhancing Batch-wise Inference Efficiency for Large Language Models via Dynamic Re-batching
Peizhuang Cong, Qizhi Chen, Haochen Zhao +1
The advanced capabilities of Large Language Models (LLMs) have inspired the development of various interactive web services or applications, such as ChatGPT, which offer query infe…
INT-FlashAttention: Enabling Flash Attention for INT8 Quantization
Shimao Chen, Zirui Liu, Zhiying Wu +6
As the foundation of large language models (LLMs), self-attention module faces the challenge of quadratic time and memory complexity with respect to sequence length. FlashAttention…
Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing
Peizhuang Cong, Aomufei Yuan, Shimao Chen +4
MoE facilitates the development of large models by making the computational complexity of the model no longer scale linearly with increasing parameters. The learning sparse gating…