Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply ...
Abstract: In this paper, we consider the model merging process for large language models (LLMs) under a two-stage optimization framework. Traditional merging methods usually apply fixed blending rates ...
Abstract: In this article, switching model predictive control (MPC) is proposed for a perturbed max-plus linear system with a reference signal. The lack of stability guarantee for MPC brings ...
What is this? This workflow quantizes diffusion model weights to lower numerical precision (4-bit, 8-bit, or float8) so they use significantly less GPU memory. A model that normally requires 32 GB of ...
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