[P] A control plane for post-training workflows

We have been exploring a project around post-training infrastructure, a minimalist tool that does one thing really well:
Make post-training a little less painful by equipping Researchers, AI/ML engineers & Tinkerers with a gentle control plane. Post-training models tends to introduce a new axis of complexity – the orchestration and compute ressource management – alongside defining your own training loop, your rewards & rubrics, managing the parallel training.

Tahuna is CLI-first, it sits between your local environment and your compute provider. You own the training loop entirely – your rollout logic, your rewards, your data pipeline. It handles the plumbing around it.

We are cleaning up the code, but we are open-sourcing the entire stack soon.

Free to use. Early stage, looking for people who want to poke at it, break it, or contribute adapters.

tahuna.app

Happy to talk implementation details or tradeoffs in the comments.