Fine-Tuning My AI Model: A Journey Towards a Digital Reflection of Myself

Over the past few months, I have been working on fine-tuning an AI model to behave like me. My approach involves training the model on my personal digital footprint, including my notes, Discord messages, weekly and monthly journals (you can read more about my content creation process here), and various other elements of my digital life.

The Initial Challenge: Data Collection and Processing

Before diving into model selection, I faced the significant challenge of collecting and processing my digital footprint. This involved:

• Extracting and formatting years of Discord messages using Discord's API
• Converting my Obsidian notes into a structured format
• Processing my journal entries from various sources (Notion, Google Docs, and plain text files)
• Implementing data cleaning pipelines to remove standardize formats

The data preprocessing stage was crucial, as it directly impacted the quality of the training data. I developed custom scripts to:

• Remove personally identifiable information
• Standardize timestamps and formatting
• Convert different file formats into a consistent structure
• Implement data augmentation techniques for underrepresented patterns

Moving from Mistral 8B to Gemma 3

Initially, I worked with the Mistral 8B model, but I encountered several limitations that made me reconsider my choice. The main challenges with Mistral 8B were:

• Limited context window (8k tokens)
• Higher memory requirements during training
• Slower inference times
• Less efficient handling of mixed media content

After discovering Gemma 3, I decided to switch to its 12B parameter version, and the difference has been remarkable. The key advantages I found with Gemma 3 include:

• Extended Context Window: 129k tokens, allowing for much longer conversations and document processing
• Multimodal Capabilities: Native support for processing images and short videos
• Language Support: Comprehensive support for 140 languages
• Efficient Training: Better memory utilization and faster convergence
• Resource Optimization: Surprisingly lightweight hosting requirements for its capabilities

The Fine-Tuning Process

Data Preparation and Training Setup

To continue refining the model, I retrieved my original dataset and updated it with my latest digital content. The training process involved:

1. Dataset Curation:

   • Creating balanced training sets from different data sources
   • Implementing stratified sampling to ensure representation of different communication styles
   • Adding metadata tags for different types of content (technical, personal, creative)

2. Training Infrastructure:

   • Setting up a cloud GPU instance (NVIDIA A100) for training
   • Implementing distributed training for faster convergence
   • Creating automated checkpoints and model versioning

3. Training Parameters:

   • Learning rate: 2e-5 with cosine decay
   • Batch size: 32 with gradient accumulation
   • Training epochs: 3 with early stopping
   • Using LoRA (Low-Rank Adaptation) for efficient fine-tuning

Knowledge Distillation and Model Improvement

After the initial training, I noticed that the model wasn't behaving exactly as expected. To address this, I implemented a sophisticated knowledge distillation pipeline:

1. Teacher-Student Framework:

   • Using the Mistral 8B model as the teacher
   • Gemma 3 as the student model
   • Implementing both soft and hard distillation techniques

2. Behavioral Alignment:

   • Creating specific loss functions for different aspects of personality
   • Implementing reinforcement learning from human feedback (RLHF)
   • Fine-tuning on specific conversation patterns

Deployment and Further Personalization

API and Infrastructure

The results have exceeded my expectations. To make the model accessible, I built:

1. Backend Infrastructure:

   • FastAPI-based REST API
   • Redis caching for frequent queries
   • Load balancing for multiple model instances
   • Monitoring and logging system

2. Frontend Development:

   • NextJS-based dashboard for model management
   • Real-time conversation interface
   • Data visualization tools for model performance
   • Automated data ingestion pipeline

Enhanced Personalization

The model now captures my communication style and thought patterns more accurately. To further improve it, I'm incorporating:

1. Additional Data Sources:

   • YouTube viewing history and comments
   • MyAnimeList profile and ratings
   • GitHub activity and code comments
   • Social media interactions

2. Continuous Learning:

   • Implementing online learning capabilities
   • Creating feedback loops for model improvement
   • Automated data collection and processing
   • Regular model evaluation and updates

Technical Challenges and Solutions

Throughout this journey, I encountered several technical challenges:

1. Memory Management:

   • Implemented gradient checkpointing
   • Used mixed precision training
   • Optimized data loading pipelines

2. Training Stability:

   • Added gradient clipping
   • Implemented learning rate warmup
   • Used weight decay for regularization

3. Deployment Optimization:

   • Model quantization for faster inference
   • Dynamic batching for efficient resource usage
   • Caching mechanisms for common queries

Looking Ahead

This journey has been both challenging and rewarding. The improvements brought by Gemma 3, combined with my fine-tuning techniques, have allowed me to create an AI model that truly mirrors my thought processes. Future plans include:

1. Model Improvements:

   • Implementing more sophisticated RLHF techniques
   • Adding support for real-time learning
   • Enhancing multimodal capabilities

2. Infrastructure Scaling:

   • Implementing Kubernetes for better resource management
   • Adding more sophisticated monitoring tools
   • Improving the data pipeline efficiency

3. Feature Expansion:

   • Adding voice interaction capabilities
   • Creating more interactive learning experiences

As I continue to refine and expand its capabilities, I am excited to see how this digital representation of myself evolves and how it can help me better understand and document my own growth and development.