Progress Pathway - 2025-12-29
Starting Point
- Goal: Train LARS local AI with identity and Nexus knowledge
- Infrastructure: local-ai server (100.89.34.86), dual RTX 3090, qwen2.5-7b-abliterated
- Initial approach: Simple 2D Q&A fine-tuning
Timeline
Step 1: 2D Training Attempt (EXP-001) - 20 Q&A pairs, 3 epochs - Result: Model still identified as Qwen - Learning: Not enough training
Step 2: Extended 2D Training (EXP-002) - Same dataset, 10 epochs, higher LR - Result: Better loss (0.05) but still says 'I am Qwen' first - Learning: 2D format insufficient for identity override
Step 3: Research Phase
- Searched for advanced training techniques
- Found DeepSeek R1 approach with
Step 4: 3D Format Design
- Created lars_3d_identity.json with thinking process
- 12 examples with
Step 5: 3D Training (EXP-003) - BREAKTHROUGH - 12 examples, 10 epochs, 3 minutes - Result: LARS identifies as LARS, shows thinking - Generalization: 4/5 novel questions correct
Current State
- Working 3D training pipeline
- LARS can think and reason about identity
- Some edge cases still confused (base model fighting)
Next Steps Identified
- Add more reinforcement examples (3 variations per concept)
- Test operational reasoning (tool selection)
- Consider tool access architecture for LARS
Key Insight
3D format with visible thinking is MORE effective than 2D, even with fewer examples (12 vs 20). The thinking process helps the model learn identity more deeply.