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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

12Bonus Labs: Hands-on with Hugging Face PEFT and QLoRA on Llama/Mistral

12.1 Lab Setup: Environment and Reproducibility12.2 Quickstart: PEFT with LoRA on Llama 212.3 QLoRA on Mistral 7B: Setup and Run12.4 Adapters in Practice on Large Models12.5 Prefix-Tuning Experiments on LLMs12.6 BitFit: Implementation and Evaluation12.7 Data Preparation for Labs12.8 Fine-Tuning a Small Model for Validation12.9 PEFT with DeepSpeed Integration12.10 8-bit Quantization Lab and QAT12.11 Evaluation of Fine-Tuned Models12.12 Deployment of Fine-Tuned Model in a Simple API12.13 Monitoring and Logging in Labs12.14 Troubleshooting Lab Issues12.15 Reproducibility and Documentation
Courses/Performance-Efficient Fine-Tuning: Mastering Scalable and Cost-Effective LLM Training (How to Tame and Train Your Draconian Language Model)/Bonus Labs: Hands-on with Hugging Face PEFT and QLoRA on Llama/Mistral

Bonus Labs: Hands-on with Hugging Face PEFT and QLoRA on Llama/Mistral

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Hands-on, lab-focused learning with real models to solidify PEFT workflows, QLoRA experimentation, and end-to-end fine-tuning that mirrors production setups.

Content

14 of 15

12.14 Troubleshooting Lab Issues

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