What Tinker handles during model fine-tuning
Thinking Machines provides training infrastructure while researchers control the data and training logic.
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Thinking Machines Lab introduced Tinker on 2025-10-01 as an API for fine-tuning language models. Researchers control their algorithms and data while the service handles distributed training infrastructure, scheduling and recovery. [1]
Decide what you need to control
The launch described support for open-weight models of different sizes, including mixture-of-experts models. A managed training service can remove infrastructure work while leaving the researcher responsible for the experiment itself. [1]
Before a run, define the behavior you want to change and keep evaluation examples separate from training examples. Record the starting model, data preparation and evaluation criteria so a later result can be compared with the original.
Tinker's launch establishes the service's intended division of responsibilities. It does not guarantee that a fine-tuned model will improve on your task. Check current model support, data terms and pricing before sending a dataset or starting a paid run.
Keep this guide
Filed under Dev tools
Sources
- Thinking Machines Tinker announcementthinkingmachines.ai
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