LLM Data Preparation and Finetuning

Course: Applying Generative AI in Quantum Computing

Lecture Date: June 20

Lecture Overview:

• Data Preparation and Fine-Tuning Techniques for LLMs

I. Introduction

• Recap of the course and the applications of generative AI in quantum computing
• Overview of LLM architecture and pre-training/fine-tuning processes

II. Data Preparation for LLMs

• Importance of data preparation for optimal model performance
• Data preprocessing methods:
  • Cleaning: Addressing noisy or incomplete data
  • Tokenization: Transforming data into suitable format for LLMs
  • Formatting: Managing different data types and large datasets
• Techniques for data augmentation to enhance training data diversity

III. Fine-Tuning Techniques for LLMs

• Role of fine-tuning in adapting pre-trained models to specific tasks or domains
• Fine-tuning strategies:
  • Transfer learning: Leveraging pre-training knowledge and representations
  • Domain adaptation: Adapting models to specific domains in quantum computing
  • Task-specific fine-tuning: Specializing model capabilities for specific tasks

IV. Practical Applications in Quantum Computing

• Examples and case studies showcasing the application of data preparation and fine-tuning
• Impact of these techniques on LLM performance in:
  • Generating quantum circuits
  • Optimizing quantum algorithms
  • Simulating quantum systems

V. Conclusion

• Comprehensive understanding of data preparation and fine-tuning for LLMs
• Enhancing skills in working with LLMs in the context of quantum computing
• Encouragement for active participation and engagement in discussions

During the lecture, we will explore various examples and discuss the practical implications of data preparation and fine-tuning techniques in the field of quantum computing. By the end of the session, you will have a comprehensive understanding of how to prepare data and fine-tune LLMs to achieve desirable outcomes in quantum computing.