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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

4.1 Data Quality vs Quantity Trade-offs4.2 Curating Data for Domain Relevance4.3 Deduplication and Noise Reduction4.4 Filtering for Safety and Compliance4.5 Active Learning for Data Selection4.6 Data Augmentation Techniques4.7 Data Versioning and Lineage4.8 Data Annotation Practices4.9 Curriculum Learning for Efficiency4.10 Data Licensing and Privacy4.11 Data-Driven Curriculum Design4.12 Handling Imbalanced Datasets4.13 Synthetic Data and Sim2Real4.14 Data Store and Pipeline Engineering4.15 Data Validation and QC

5Quantization, Pruning, and Compression

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

7Evaluation, Validation, and Monitoring

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)/Data Efficiency and Curation

Data Efficiency and Curation

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Strategies to source, curate, and manage high-quality data for fine-tuning, including data selection, augmentation, privacy, licensing, and versioning to maximize utility per labeled example.

Content

14 of 15

4.14 Data Store and Pipeline Engineering

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