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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)

6.1 Distributed Training Architectures Overview6.2 Data Parallelism vs Model Parallelism6.3 ZeRO Partitions and Optimizations6.4 DeepSpeed Engine Architecture6.5 Fully Sharded Data Parallel (FSDP) Fundamentals6.6 Activation Checkpointing Strategies6.7 Memory Offloading and CPU-GPU Overlap6.8 Pipeline Parallelism and Micro-batching6.9 ZeRO-2 vs ZeRO-36.10 Expert Parallelism and MoE6.11 Gradient Accumulation Across Nodes6.12 Fault Tolerance in Large-Scale Training6.13 Networking Substrates (InfiniBand, NVLink)6.14 Scheduling and Orchestrators (Kubernetes)6.15 Mixed-Precision Across Distributed

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

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

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Advanced distributed training strategies to scale fine-tuning across multiple GPUs and nodes while managing memory, communication, and fault tolerance.

Content

9 of 15

6.9 ZeRO-2 vs ZeRO-3

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