Tinker exposes distributed fine-tuning behind your own Python training loop
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Introducing Tinker: a flexible API for fine-tuning language models. Write training loops in Python on your laptop; we'll run them on distributed GPUs. Private beta starts today. We can't wait to see what researchers and developers build with cutting-edge open models!
Tinker advances our mission of enabling more people to do research on cutting-edge models and customize them to their needs. https://t.co/raQkVTiUNB
Context
Thinking Machines's Tinker separates control of a model-training experiment from management of the computers running it. Researchers write Python training loops and choose their data and algorithms; the service runs the work on distributed GPU infrastructure. The October 2025 launch article assigns scheduling, resource allocation and recovery from failures to the service.
Tinker exposes operations for training and generating model outputs that researchers can combine into their own methods. It uses , which trains model adaptations while sharing underlying compute across runs. The useful flexibility is control over the experiment without having to operate the training cluster.
- LoRA — A cheap way to fine-tune a model by training a small add-on layer instead of changing all of the model's weights.
- fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
- open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
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