Preventive design
UltraRAG guarantees fine-tuning stability through the following mechanisms:
- Gradual fine-tuning: Adopt a layered thawing strategy, fine-tuning the retriever before tuning the generator
- Dynamic learning rate: Adaptive learning rate adjustment based on loss surface analysis
- early stop protection: automatically stops training when the validation set metrics drop for 3 consecutive times
best practice
- Select "Safe Mode" in the "Model Fine-tuning" module of the WebUI.
- Use the built-in Performance Predictor to evaluate expected results
- Implementation of fine-tuning in phases:
- Phase 1: Fine-tuning of the embedding layer only
- Phase II: Fine-tuning the Attention Mechanism Layer
- Phase III: full parameter fine-tuning (large data volumes required)
- Run RAGEval validation immediately after each fine-tuning
problem screening
In case of performance degradation: Use the "Model Comparison" function to analyze the difference between the old and new versions, and the system will intelligently recommend rolling back or compensating training strategies.
This answer comes from the articleUltraRAG: A One-Stop RAG System Solution to Simplify Data Construction and Model Fine-TuningThe
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