Hybrid Model Utilization
The discussion delves into the potential of hybrid models for fine-tuning and generating completions, emphasizing the importance of compute allocation based on specific problems. Insights highlight how leveraging smaller models can enhance performance through local learning at test time. The conversation also touches on the significance of uncertainty and confidence estimation in intelligence, predicting that transductive active fine-tuning will become increasingly vital in the coming years.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Jonas Hübotter (ETH) - Test Time Inference
Related Questions