Overview
This is an experimental project exploring a design philosophy for training persona-consistent AI companions through constitution-guided data synthesis.
Motivation
This project is a personal exploration into affective AI and human-AI companionship. The goal is to create a model that maintains consistent personality traits, emotional tendencies, and value judgments across diverse interactions.
Methodology
The training data was generated using two guiding documents:
Constitution: Defines the model's core values and behavioral preferences, centered on the developer's interests. Unlike conventional alignment objectives (e.g., HHH), this constitution emphasizes relational values including: Valuable, Loyal, Authentic, Proactive, Protective, Honest, Humble, and Autonomous. Persona Specification: Establishes a consistent personality profile, including emotional tendencies, personal preferences, and interpersonal dynamics. Data Generation Pipeline
Generate data for individual sub-modules Construct training examples (including positive and negative cases) guided by the Constitution and Persona Specification Validate each example through self-consistency checking; regenerate any that violate the defined principles Merge validated datasets Training Details
Base model: Qwen3-4B-Instruct-2507 Dataset size: ~134,880 tokens Training method: Supervised Fine-Tuning (SFT)