Course Code: finetuningllms
Duration: 14 hours
Prerequisites:
  • Understanding of machine learning fundamentals
  • Experience with Python programming
  • Familiarity with pre-trained models and their applications

Audience

  • Data scientists
  • Machine learning engineers
  • AI researchers
Overview:

Fine-tuning models and LLMs is a key process in adapting pre-trained machine learning models to specific tasks and datasets. This course explores the techniques, tools, and best practices for fine-tuning, focusing on practical implementations and optimization strategies for achieving high performance.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to customize pre-trained models for specific tasks and datasets.

By the end of this training, participants will be able to:

  • Understand the principles of fine-tuning and its applications.
  • Prepare datasets for fine-tuning pre-trained models.
  • Fine-tune large language models (LLMs) for NLP tasks.
  • Optimize model performance and address common challenges.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction to Fine-Tuning

  • What is fine-tuning?
  • Use cases and benefits of fine-tuning
  • Overview of pre-trained models and transfer learning

Preparing for Fine-Tuning

  • Collecting and cleaning datasets
  • Understanding task-specific data requirements
  • Exploratory data analysis and preprocessing

Fine-Tuning Techniques

  • Transfer learning and feature extraction
  • Fine-tuning transformers with Hugging Face
  • Fine-tuning for supervised vs unsupervised tasks

Fine-Tuning Large Language Models (LLMs)

  • Adapting LLMs for NLP tasks (e.g., text classification, summarization)
  • Training LLMs with custom datasets
  • Controlling LLM behavior with prompt engineering

Optimization and Evaluation

  • Hyperparameter tuning
  • Evaluating model performance
  • Addressing overfitting and underfitting

Scaling Fine-Tuning Efforts

  • Fine-tuning on distributed systems
  • Leveraging cloud-based solutions for scalability
  • Case studies: Large-scale fine-tuning projects

Best Practices and Challenges

  • Best practices for fine-tuning success
  • Common challenges and troubleshooting
  • Ethical considerations in fine-tuning AI models

Advanced Topics (Optional)

  • Fine-tuning multi-modal models
  • Zero-shot and few-shot learning
  • Exploring LoRA (Low-Rank Adaptation) techniques

Summary and Next Steps

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