Overview

Using Ollama with the deepseek-r1:1.5b model, I generated a SILO paper QA dataset in JSONL format. After fine-tuning the model with this dataset, the response quality turned out to be quite satisfactory, demonstrating the effectiveness of the approach. However, throughout the process, it became clear that there is still significant room for improvement, particularly regarding the PDF chunking strategy.

Limitations and Reflections

The way the PDF documents are segmented and preprocessed plays a critical role in determining the overall performance of the system. This experience reaffirmed the importance of data preparation as the most crucial factor in achieving high-quality results in fine-tuning tasks. Proper chunking not only ensures that relevant context is preserved but also minimizes information loss, which directly impacts the model's ability to generate accurate and coherent answers. Moving forward, enhancing the chunking methodology will likely lead to even greater improvements in model performance, further emphasizing that in machine learning workflows, the quality of input data preparation often outweighs even model architecture enhancements.