README.md

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# Tinman Lab

### Autonomous Machines. Second-Order Systems.

<sub>AGENT MEMORY · ADVERSARIAL SAFETY · AGENTIC ECONOMY · PERCEPTION SYSTEMS · APPLIED RESEARCH</sub>

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## 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](https://huggingface.co/Tinman-Lab/tinman-code-0.6B) | 418 MB | Coding assistant with meta-cognitive awareness — plans, verifies, flags uncertainty |

## Links

- [Website](https://tinmanlab.com)
- [GitHub](https://github.com/tinmanlabsl/)
- Research Paper — coming soon