MultiHub Forum

I'm a machine learning engineer working on fine-tuning a large language model for a specific document analysis task, and while I understand the high-level concept of the transformer architecture, I'm struggling with the practical implications of modifying attention mechanisms for much longer context windows than the base model was trained on. The computational cost is becoming prohibitive. For others who have worked on adapting these architectures, what strategies or recent variations like efficient attention have you found most effective for handling long sequences without a complete model redesign, and how did you approach the trade-off between context length, accuracy, and training/inference time?