no code implementations • 13 Jul 2022 • Mukul Gagrani, Corrado Rainone, Yang Yang, Harris Teague, Wonseok Jeon, Herke van Hoof, Weiliang Will Zeng, Piero Zappi, Christopher Lott, Roberto Bondesan
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance.
1 code implementation • 27 Apr 2023 • Burak Bartan, Haoming Li, Harris Teague, Christopher Lott, Bistra Dilkina
The deployment and training of neural networks on edge computing devices pose many challenges.
no code implementations • 21 Feb 2024 • Wonseok Jeon, Mukul Gagrani, Raghavv Goel, Junyoung Park, Mingu Lee, Christopher Lott
We empirically evaluate RSD with Llama 2 and OPT models, showing that RSD outperforms the baseline methods, consistently for fixed draft sequence length and in most cases for fixed computational budgets at LLM.
no code implementations • 29 Feb 2024 • Raghavv Goel, Mukul Gagrani, Wonseok Jeon, Junyoung Park, Mingu Lee, Christopher Lott
In this paper, we propose a simple draft model training framework for direct alignment to chat-capable target models.