Gain advanced proficiency in customizing pre-trained Large Language Models (LLMs) for specific tasks and domains. This course covers sophisticated fine-tuning methodologies, parameter-efficient techniques (PEFT), data preparation strategies, and rigorous evaluation practices. Learn to optimize LLM performance, manage computational resources effectively, and adapt models to specialized requirements through hands-on implementation.
Prerequisites ML & Transformer Basics
Level:
Advanced Fine-tuning Strategies
Implement and differentiate between full fine-tuning and various Parameter-Efficient Fine-Tuning (PEFT) methods.
Data Preparation for Adaptation
Develop sophisticated datasets for instruction tuning and domain adaptation, addressing data quality and quantity challenges.
PEFT Implementation
Apply techniques such as LoRA, QLoRA, and Adapter modules to efficiently adapt large models.
Training Optimization
Optimize the fine-tuning process for computational efficiency using techniques like gradient accumulation and mixed-precision training.
Model Evaluation
Perform in-depth evaluation of fine-tuned models, assessing performance beyond standard metrics, including instruction following and bias analysis.
Deployment Considerations
Analyze the practical aspects of deploying fine-tuned LLMs, including model serialization and serving strategies.