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title: README
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Tinman Lab
Autonomous Machines. Second-Order Systems.
AGENT MEMORY · ADVERSARIAL SAFETY · AGENTIC ECONOMY · PERCEPTION SYSTEMS · APPLIED RESEARCH
Disposition Distillation
Tinman Lab develops Disposition Distillation (DD) — a multi-teacher distillation methodology that trains how a model behaves into weights, not system prompts. DD models plan before acting, acknowledge uncertainty, verify their own reasoning, and know what they don't know.
- 4-stage all-MIT pipeline — Kimi K2.5 → GLM-5 → MiniMax M2.7 → GLM-5
- 7 behavioral dispositions — Eager, Deliberate, Adversarial, Curious, Self-Improving, Humble, Persistent
- On-device focus — 0.6B to 2B parameters, quantized for mobile and edge deployment
- 100% open training data — MIT-licensed teachers only, zero proprietary model outputs
Models
| Model | Size | Description |
|---|---|---|
| tinman-code-0.6B | 418 MB | Coding assistant with meta-cognitive awareness — plans, verifies, flags uncertainty |