Methodology

Research Design

Our research follows a systematic approach to fine-tuning language models using LoRA, with a specific focus on capturing Warren Buffett’s investment philosophy and analysis style.

Data Collection

1. Source Materials: - Annual letters to Berkshire Hathaway shareholders - Transcripts of interviews and speeches - Books and articles about Buffett’s investment philosophy - Public statements and market commentary

2. Data Processing: - Text cleaning and normalization - Content categorization - Quality filtering - Format standardization

LoRA Implementation

1. Base Model Selection: - Criteria for choosing the foundation model - Model architecture considerations - Size and computational requirements

2. LoRA Configuration: - Rank selection - Alpha parameter tuning - Target modules selection - Learning rate optimization

Training Process

1. Hyperparameter Selection: - Learning rate scheduling - Batch size optimization - Training duration - Validation approach

2. Training Infrastructure: - Google Colab (A100 GPU) - Python, PyTorch, Hugging Face

Evaluation Framework

1. Quantitative Metrics:

2. Qualitative Assessment: