This course delivers a comprehensive, engineer-friendly blueprint for fine-tuning large language models with an emphasis...
Establish the core concepts, paradigms, and baseline practices that underlie effective fine-tuning of LLMs, including training objectives, data considerations, and diagnostic visuals to set a solid foundation for scalable optimization.
Techniques to maximize throughput and accuracy while minimizing GPU, memory, and energy costs through profiling, memory management, data pipelines, and scheduling strategies.
In-depth exploration of PEFT techniques (LoRA, QLoRA, Adapters, Prefix-tuning, BitFit) with guidance on method selection, stability, and integration with other optimization strategies.
Strategies to source, curate, and manage high-quality data for fine-tuning, including data selection, augmentation, privacy, licensing, and versioning to maximize utility per labeled example.
Techniques to shrink models and accelerate inference—quantization, pruning, distillation, and end-to-end compression pipelines with attention to accuracy, latency, and hardware support.
Advanced distributed training strategies to scale fine-tuning across multiple GPUs and nodes while managing memory, communication, and fault tolerance.
Rigorous evaluation frameworks, validation strategies, and monitoring dashboards to ensure robust performance, safety, and reproducibility across deployments.
From domain adaptation to production deployment, this module covers end-to-end workflows, including serving, observability, safety, and governance in real-world use cases.
Exploration of next-generation techniques shaping how we adapt and scale LLMs, including MoE, retrieval-augmented strategies, continual learning, and cross-cutting tools.
A focused module on building reliable, end-to-end validation and debugging workflows, ensuring reproducibility and rapid incident response in real-world pipelines.
Economic and operational perspectives to plan, monitor, and optimize the total cost of ownership for fine-tuning projects, from capex to opex.
Hands-on, lab-focused learning with real models to solidify PEFT workflows, QLoRA experimentation, and end-to-end fine-tuning that mirrors production setups.