Search Results for author: Aimin Pan

Found 3 papers, 1 papers with code

FlattenQuant: Breaking Through the Inference Compute-bound for Large Language Models with Per-tensor Quantization

no code implementations28 Feb 2024 Yi Zhang, Fei Yang, Shuang Peng, Fangyu Wang, Aimin Pan

The 4-bit matrix multiplication introduced in the FlattenQuant method can effectively address the compute-bound caused by large matrix calculation.

Quantization

Holmes: Towards Distributed Training Across Clusters with Heterogeneous NIC Environment

no code implementations6 Dec 2023 Fei Yang, Shuang Peng, Ning Sun, Fangyu Wang, Ke Tan, Fu Wu, Jiezhong Qiu, Aimin Pan

Large language models (LLMs) such as GPT-3, OPT, and LLaMA have demonstrated remarkable accuracy in a wide range of tasks.

Scheduling

Exploring Post-Training Quantization of Protein Language Models

1 code implementation30 Oct 2023 Shuang Peng, Fei Yang, Ning Sun, Sheng Chen, Yanfeng Jiang, Aimin Pan

In summary, our study introduces an innovative PTQ method for ProteinLMs, addressing specific quantization challenges and potentially leading to the development of more efficient ProteinLMs with significant implications for various protein-related applications.

Protein Structure Prediction Quantization

Cannot find the paper you are looking for? You can Submit a new open access paper.