Post Training

Post-training techniques aim to improve or adapt pre-trained machine learning models without requiring extensive retraining, offering significant computational and time savings. Current research focuses on diverse methods including quantization (e.g., using algorithms like GPTQ and CDQuant) to reduce model size and computational cost, adaptive inference strategies (like early exiting and input-dependent compression) to optimize resource usage, and techniques to enhance model alignment and mitigate issues like unintended sophistry in large language models. These advancements are crucial for deploying large models on resource-constrained devices and improving the efficiency and reliability of AI systems across various applications.