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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)

9.1 Mixture of Experts (MoE) Architectures9.2 Retrieval-Augmented Fine-Tuning (RAG) Workflows9.3 Continual/Lifelong Fine-Tuning9.4 Dynamic and Conditional Computation9.5 Cross-Modal Fine-Tuning and Tool Integration9.6 Federated Fine-Tuning and Privacy-Preserving Methods9.7 Differential Privacy in Fine-Tuning9.8 Knowledge Distillation for Efficiency9.9 MoE Load Balancing and Expert Selection9.10 Dialog and Multi-Agent Fine-Tuning Scenarios9.11 Meta-Learning for Rapid Adaptation9.12 Continual Data Integration Strategies9.13 Benchmarking for Emerging Methods9.14 Robustness and Safety Considerations9.15 Ecosystem and Tooling Evolution

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

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

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Exploration of next-generation techniques shaping how we adapt and scale LLMs, including MoE, retrieval-augmented strategies, continual learning, and cross-cutting tools.

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5 of 15

9.5 Cross-Modal Fine-Tuning and Tool Integration

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