Performance-Efficient Fine-Tuning: Mastering Scalable and Cost-Effective LLM Training (How to Tame and Train Your Draconian Language Model)

What you'll learn

• Design end-to-end, memory- and cost-efficient fine-tuning pipelines for large language models
• Apply parameter-efficient fine-tuning (PEFT) methods including adapters, LoRA, and QLoRA
• Implement quantization, pruning, and compression strategies that preserve model quality
• Configure and run distributed fine-tuning with DeepSpeed, FSDP, and ZeRO for large-scale models
• Curate and preprocess training data to maximize downstream performance and reduce compute
• Build evaluation, validation, and monitoring pipelines to detect regressions and drift
• Debug, verify, and reproduce fine-tuning experiments with best-practice tooling
• Model and optimize costs—budgeting, benchmarking, and trade-off analysis for production
• Deploy and operate fine-tuned models in production with observability and safety checks
• Hands-on experience with Hugging Face PEFT tooling and QLoRA on representative models

Prerequisites

Comfortable with Python and basic deep learning (PyTorch/TensorFlow), familiarity with transformer models and command-line tools; prior GPU experience recommended.

Skills you'll gain

• Parameter-efficient fine-tuning
• Quantization and compression
• Model pruning strategies
• Distributed training (DeepSpeed, FSDP, ZeRO)
• Data curation and efficiency
• Evaluation and monitoring
• Experiment debugging and validation
• Cost modeling and budgeting
• Production deployment and observability
• Hugging Face PEFT and QLoRA workflow