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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

2.1 Profiling CPU, GPU, and I/O Bottlenecks2.2 Memory Footprint Reduction Techniques2.3 Throughput and Latency Trade-offs2.4 Batch Sizing and Gradient Accumulation2.5 Mixed-Precision Training and Numerical Stability2.6 Activation Sparsity and Operator Fusion2.7 Data Pipeline Optimization and Prefetching2.8 Storage Layouts and Data Caching2.9 Offloading and CPU-GPU Overlap2.10 Model Sharding vs Data Parallelism2.11 Asynchronous vs Synchronous Gradient Updates2.12 Checkpointing, Resume, and Fault Tolerance2.13 Energy Efficiency and Cooling Considerations2.14 Hot-Cold Memory Management2.15 Auto-Scaling Strategies for Training Slots

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

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)/Performance and Resource Optimization

Performance and Resource Optimization

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Techniques to maximize throughput and accuracy while minimizing GPU, memory, and energy costs through profiling, memory management, data pipelines, and scheduling strategies.

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5 of 15

2.5 Mixed-Precision Training and Numerical Stability

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