Prerequisites: Solid understanding of machine learning concepts, deep learning principles, Python programming proficiency. Experience with deep learning frameworks (PyTorch/TensorFlow) and familiarity with transformer architectures and foundational LLM concepts are assumed.

Level: Advanced

What You'll Learn

• 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.