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Starts 3 June 2025 06:10

Ends 3 June 2025

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Data Curation for Open Source LLM Fine-Tuning

Explore data curation challenges and strategies for fine-tuning open source LLMs, focusing on dataset quality and iteration techniques for improved results.
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Overview

Explore data curation challenges and strategies for fine-tuning open source LLMs, focusing on dataset quality and iteration techniques for improved results.

Syllabus

  • Introduction to Data Curation for LLM Fine-Tuning
  • Overview of Large Language Models (LLMs)
    Importance of Data Curation in Fine-Tuning
    Objective and Scope of the Course
  • Understanding Open Source LLMs
  • Popular Open Source LLM Platforms and Tools
    Advantages and Limitations of Open Source Models
  • Data Quality for Fine-Tuning
  • Characteristics of High-Quality Datasets
    Data Quality Metrics and How to Measure Them
    Identifying and Reducing Bias in Data
  • Data Collection and Preprocessing
  • Sources for Collecting Data
    Data Cleaning Techniques
    Text Preprocessing for LLMs
  • Data Curation Strategies
  • Manual vs. Automated Curation Methods
    Use of Metadata for Enhanced Curation
    Versioning and Iterative Improvement of Datasets
  • Iteration Techniques for Improved Results
  • Feedback Loops in Fine-Tuning
    Continuous Integration of New Data
    Evaluation and Testing of Fine-Tuned Models
  • Tools and Technologies for Data Curation
  • Overview of Data Annotation Tools
    Employing AI and ML Tools for Data Curation
    Open Source Libraries and Frameworks
  • Case Studies and Real-World Applications
  • Examples of Successful Data Curation Projects
    Analysis of Failures and Lessons Learned
  • Ethical Considerations in Data Curation
  • Addressing Privacy and Security Concerns
    Ensuring Transparency and Accountability
  • Conclusion and Future Directions
  • Emerging Trends in Data Curation for LLMs
    Course Recap and Final Thoughts
  • Capstone Project
  • Design and Implement a Data Curation Workflow for Fine-Tuning
    Peer Review and Feedback Sessions

Subjects

Data Science