# 05 — Customization & safety

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## Why open weights + Tinker

Thinking Machines’ bet: specialized knowledge closes the gap generalists leave. **Tinker** is the managed post-train API; open weights let you export/deploy elsewhere.

Launch narrative: model **self-fine-tunes** (lipogram, no letter *e*) via Tinker — behavior prompting alone cannot enforce.

| Resource | URL |
|---|---|
| Tinker | https://thinkingmachines.ai/tinker/ |
| Cookbook | https://github.com/thinking-machines-lab/tinker-cookbook |
| Docs | https://tinker-docs.thinkingmachines.ai/cookbook/inkling/ |
| Renderers | `tml-renderers` on PyPI |

Context options on Tinker at launch: 64K / 256K (verify current docs).

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## Deploy partners (release)

Hosted: Together, Fireworks, Modal, Databricks, Baseten.  
Open: SGLang+Miles, vLLM, TokenSpeed, llama.cpp, transformers.

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## Post-train directions

1. Domain SFT / LoRA on private data  
2. Agent RL (OpenEnv-style environments)  
3. Distill to smaller on-device students (cross-tokenizer methods e.g. GOLD/TRL)  

Always pack with the official chat template + `tml-renderers` so tools/effort/media stay on-distribution.

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## Deployer checklist

Residual risks they call out:

- Role-play / indirect harmful framing  
- Hallucination, multi-turn drift, data bias  
- Cutoff knowledge  
- Uneven language/domain coverage  

Do:

1. Eval on **your** distribution  
2. App-layer filter / rate limit / monitor  
3. External guard models where needed  
4. Human oversight for high-stakes domains  
5. No unsupervised medical/legal/safety-critical use without domain FT + validation  

Policy: https://thinkingmachines.ai/model-acceptable-use-policy/

Next: [06-study-plan.md](06-study-plan.md)