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Performance-Efficient Fine-Tuning: Mastering Scalable and Cost-Effective LLM Training (How to Tame and Train Your Draconian Language Model)
Chapters

1Foundations of Fine-Tuning

2Performance and Resource Optimization

3Parameter-Efficient Fine-Tuning Methods

4Data Efficiency and Curation

5Quantization, Pruning, and Compression

6Scaling and Distributed Fine-Tuning (DeepSpeed, FSDP, ZeRO)

7Evaluation, Validation, and Monitoring

7.1 Evaluation Protocols for Fine-Tuning7.2 Validation Set Design and Splits7.3 Baselines and Reference Models7.4 Probing and Interpretability Techniques7.5 Robustness and Safety Evaluation Methods7.6 Traditional Metrics: Perplexity, BLEU, ROUGE7.7 Human-in-the-Loop Assessment7.8 Online vs Offline Evaluation Strategies7.9 Monitoring Dashboards and Alerts7.10 Experiment Tracking with Reproducibility7.11 Resource Utilization and Efficiency Metrics7.12 Data Drift Detection in Evaluation7.13 A/B Testing for Fine-Tuning7.14 Calibration and Uncertainty Estimation7.15 Fairness and Bias Evaluation

8Real-World Applications and Deployment

9Future of Fine-Tuning (Mixture of Experts, Retrieval-Augmented Fine-Tuning, Continual Learning)

10Practical Verification, Debugging, and Validation Pipelines

11Cost Modeling, Budgeting, and Operational Efficiency

12Bonus Labs: Hands-on with Hugging Face PEFT and QLoRA on Llama/Mistral

Courses/Performance-Efficient Fine-Tuning: Mastering Scalable and Cost-Effective LLM Training (How to Tame and Train Your Draconian Language Model)/Evaluation, Validation, and Monitoring

Evaluation, Validation, and Monitoring

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Rigorous evaluation frameworks, validation strategies, and monitoring dashboards to ensure robust performance, safety, and reproducibility across deployments.

Content

6 of 15

7.6 Traditional Metrics: Perplexity, BLEU, ROUGE

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