Chapter 8: Fine-Tuning & Domain Adaptation

Introduction: Making General Models Domain-Specific

Part I: Why Fine-Tuning Works for Science

The Transfer Learning Paradigm

What Gets Learned During Fine-Tuning?

Part II: Parameter-Efficient Fine-Tuning (PEFT)

The Challenge: Limited Resources

Low-Rank Adaptation (LoRA)

Comparison of PEFT Methods

Part III: Practical Results - Biology Text Fine-Tuning

Experimental Setup

Results: 90.3% Perplexity Improvement

Text Generation Comparison

Key Findings

Part IV: Preparing Domain-Specific Training Data

Building Scientific Corpora

Data Quality Control

Part V: Evaluation and Validation

Domain-Specific Metrics

Evaluation Framework

Secondary Metrics

Note: The dataset updates in real time, so each run may yield different results.

Part VI: Best Practices and Lessons Learned

Hyperparameter Selection

Training Tips

Resource Management

Summary

References

Additional Resources