What You Need to Know Before
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Starts 3 June 2025 06:10
Ends 3 June 2025
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19 minutes
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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
- Understanding Open Source LLMs
- Data Quality for Fine-Tuning
- Data Collection and Preprocessing
- Data Curation Strategies
- Iteration Techniques for Improved Results
- Tools and Technologies for Data Curation
- Case Studies and Real-World Applications
- Ethical Considerations in Data Curation
- Conclusion and Future Directions
- Capstone Project
Overview of Large Language Models (LLMs)
Importance of Data Curation in Fine-Tuning
Objective and Scope of the Course
Popular Open Source LLM Platforms and Tools
Advantages and Limitations of Open Source Models
Characteristics of High-Quality Datasets
Data Quality Metrics and How to Measure Them
Identifying and Reducing Bias in Data
Sources for Collecting Data
Data Cleaning Techniques
Text Preprocessing for LLMs
Manual vs. Automated Curation Methods
Use of Metadata for Enhanced Curation
Versioning and Iterative Improvement of Datasets
Feedback Loops in Fine-Tuning
Continuous Integration of New Data
Evaluation and Testing of Fine-Tuned Models
Overview of Data Annotation Tools
Employing AI and ML Tools for Data Curation
Open Source Libraries and Frameworks
Examples of Successful Data Curation Projects
Analysis of Failures and Lessons Learned
Addressing Privacy and Security Concerns
Ensuring Transparency and Accountability
Emerging Trends in Data Curation for LLMs
Course Recap and Final Thoughts
Design and Implement a Data Curation Workflow for Fine-Tuning
Peer Review and Feedback Sessions
Subjects
Data Science