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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

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

11.1 Total Cost of Ownership for Fine-Tuning11.2 GPU Utilization and Cost Analytics11.3 Data Storage and Transfer Costs11.4 Budgeting Experiments with Cost Caps11.5 Cloud vs On-Prem Cost Trade-offs11.6 Licensing and Tooling Costs11.7 Energy Efficiency and Sustainability Metrics11.8 ROI and Cost-Performance Trade-offs11.9 Cost-Aware Hyperparameter Tuning11.10 Inference Serving Cost Modeling11.11 Resource Reservation and Auto-Scaling11.12 Cost Monitoring Dashboards11.13 Financial Risk and Compliance11.14 Vendor Negotiation with Tooling Suppliers11.15 Budgeting for Bug Bashes and Spikes

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)/Cost Modeling, Budgeting, and Operational Efficiency

Cost Modeling, Budgeting, and Operational Efficiency

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Economic and operational perspectives to plan, monitor, and optimize the total cost of ownership for fine-tuning projects, from capex to opex.

Content

7 of 15

11.7 Energy Efficiency and Sustainability Metrics

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