Data Curation for Open Source LLM Fine-Tuning

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

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Subjects

Data Science