Knowledge Distillation & Fine-Tuning

Technical Implementation

Live Local Models (Ollama)

Currently running on local RTX 4070 Super GPU:

• rune-ai-v1:latest (4.4 GB) — Production multi-agent orchestrator, trained on RUNE HQ conventions
• claude-jr:latest (4.4 GB) — Specialized domain agent distilled from premium models
• mistral:7b (4.4 GB) — Base model for fine-tuning experiments

Teacher-Student Architecture

Systematic knowledge transfer from large models to optimized edge deployments.

• Temperature-Scaled Learning: High-temperature teacher outputs provide probabilistic guidance for student training
• Synthetic Data Generation: Teacher model produces high-quality training examples for student fine-tuning
• Cross-Entropy Minimization: Students learn to mimic teacher distribution, not ground truth directly
• Multi-Task Learning: Single student handles classification, reasoning, and generation simultaneously

Fine-Tuning Pipeline

Domain-specific adaptation ensuring models understand jewelry terminology, grading standards, and asset valuation nuances.

• Custom Tokenizer: Adds specialized tokens for gemstone types, certifications, market conditions
• Prompt Engineering: Systematic in-context learning templates for consistent outputs
• Quality Metrics: Automated evaluation against human-expert annotations
• Version Control: Git-tracked model snapshots enabling rollback to proven versions

Deployment Optimization

Techniques for reducing inference latency while maintaining accuracy across production endpoints.

• Quantization: 8-bit precision enables 4x memory reduction; negligible accuracy loss
• Model Pruning: Remove redundant parameters to achieve 70% size reduction
• Batch Inference: Process 1000+ asset valuations per call for throughput optimization
• Hardware Acceleration: GPU caching and TensorRT optimization for sub-100ms latency

Training Data Architecture

Real training curriculum extracted from premium model outputs (Opus 4.5, GPT-5.1, Sonnet 4.5):

Multi-Model Synthesis

• Claude Opus 4.5: Architectural philosophy, game state management, system design patterns
• GPT-5.1 Codex: Production-grade code quality, component libraries, scalable React patterns
• Claude Sonnet 4.5: Performance optimization, real-time game loops, memory profiling

Training Curriculum Structure

• GameState Management: Centralized state objects, hub-and-spoke architecture, no circular dependencies
• Game Loop Pattern: Fixed timestep physics (60 FPS), variable rendering, deltaTime in seconds not milliseconds
• Anti-Patterns: No ES6 classes for game state, no prop drilling, no scattered configuration values
• System Architecture: Independent modules that read/write GameState directly, init in dependency order