Hello everyone, I'd like to discuss some of the key challenges we face when developing LLM-based projects together. For instance, how can we optimize data quality and diversity to directly enhance model performance? Or what methods are most effective for addressing technical difficulties like overfitting and gradient explosion during fine-tuning? Which steps prove beneficial when starting with small datasets and scaling up? It wouldn't hurt to share our experiences and focus on the aspects that slow down development the most. Do you have any pain points or questions that you're grappling with?
What are the most important breakthroughs in LLM development?
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Pay attention to using clean, diverse, and well-labeled data for data quality. To prevent overfitting, you can combine transfer learning with early stopping and dropout on small datasets. Try gradient clipping and a lower learning rate to combat gradient explosion.