Building My Own GPT-2 Model: A Fulfilling Journey in Sorting Kindle Notes
Building a GPT-2-based model from scratch was one of the most fulfilling projects I’ve ever undertaken. It wasn’t just about diving into the mechanics of machine learning but also about solving a problem close to my heart—organizing the scattered, unstructured mass of notes from my Kindle. Like many avid readers, I’d highlight fascinating insights, jot down fleeting thoughts, and save poignant quotes during my reading sessions. Over time, these notes piled up into an unwieldy collection, full of potential but impossible to navigate effectively. This is where my journey into AI stepped in, offering a solution as elegant as it was challenging to create.
The process began with understanding GPT-2 itself. GPT-2, short for “Generative Pretrained Transformer 2,” is a language model that thrives on predicting the next word in a sequence based on prior context. It was an incredible foundation to work with, but adapting it to my specific task—categorizing and summarizing Kindle notes—required extensive customization. I liken the experience to inheriting a powerful tool and needing to tweak it into a precision instrument for a niche purpose. Before I could even think of customization, I had to get my hands dirty setting up the model environment, training datasets, and ensuring my local system could handle the workload. These steps alone were a masterclass in patience and problem-solving.
The core challenge was preparing the data. Kindle exports notes in a relatively raw format, often combining highlights and notes without clear structure. To make the data model-friendly, I wrote preprocessing scripts to clean, tokenize, and label the notes. Each piece of text had to be sorted into categories like “Quote,” “Actionable Insight,” or “Personal Thought,” and formatted as input-output pairs for training. This step was eye-opening: I gained a newfound appreciation for how much effort goes into organizing raw data into a machine-readable format. If the training phase was like teaching a child to recognize patterns, then data preparation was akin to assembling the teaching materials.
Training the GPT-2 model itself was an iterative and rewarding process. I started with transfer learning, fine-tuning the pretrained model on my curated dataset of Kindle notes. Watching the model evolve over epochs felt like sculpting—gradually chiseling away at its initial, generic understanding until it could respond intelligently and contextually to my data. After weeks of trial and error, the model reached a point where it could reliably summarize a long Kindle note into a concise insight or suggest thematic categories. The first time it categorized a quote into “Motivational” with eerie accuracy, I knew I was onto something truly special.
Deploying the model was the cherry on top. I built a simple Python application with a GUI to interface with the model. Now, whenever I add new Kindle notes, the app processes them, assigns categories, and even provides summaries for easy review. What was once a chaotic list of texts has become a well-organized, dynamic repository of knowledge I can quickly access. Beyond just sorting my notes, this system has reignited my passion for revisiting and applying what I’ve read. Books I thought I’d forgotten are now living, breathing parts of my intellectual toolkit.
Looking back, building this model was much more than just a technical project. It was a deeply personal journey of learning and creation, one that merged my love for technology and literature. The experience taught me not just about neural networks and transformers, but also about the incredible satisfaction of taking an idea from a simple “What if?” to a functional reality. It reminded me that some of the best projects are those that solve problems you care deeply about, turning chaos into clarity in a way that feels almost magical.