Lowering Costs and Increasing Benefits Through the Ensemble of LLMs and Machine Learning Models

Author:

Liu Zhilin,Yang Qun,Zou Jianjian

Publisher

Springer Nature Singapore

Reference21 articles.

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3. Xiao, G., Lin, J., Seznec, M., Wu, H., Demouth, J., Han, S.: SmoothQuant: accurate and efficient post-training quantization for large language models. In: 40th International Conference on Machine Learning, ICML 2023, pp. 38087–38099

4. Frantar, E., Ashkboos, S., Hoefler, T., Alistarh, D.: GPTQ: accurate post-training quantization for generative pre-trained transformers. arXiv (2022)

5. Hinton, G.E., Vinyals, O., Dean, J.M.: Distilling the knowledge in a neural network. arXiv: Machine Learning (2015)

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