Tahuna
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The future is not a single intelligent blob.
It's billions of species of models.

The future of artificial intelligence is a wide field of specialized systems. Post-training infrastructure should be easily accessible.

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Works with your stack

Works with your stack
PyTorchPyTorchHuggingFaceHuggingFaceUnslothUnslothTRLTRLPrime IntellectPrime Intellect
InstallTahuna

A gentle control plane
for post-training

Post-training is the new frontier, but the infrastructure hasn't caught up yet.

Tahuna is a gentle control plane for post-training that keeps your code and your loop intact while handling provisioning, sync, dependencies, monitoring, and artifacts — so you can focus on AI agent training instead of DevOps.

tahuna train
>$ tahuna init .
config def
entrypoint detected, environment scaffolded
>$ tahuna sync
syncing code, data, env config
>$ tahuna train
materializing
finetuning minimax2.5
streaming metrics
runtime/wardenwandb-compatible metrics

curl -fsSL https://tahuna.app/install.sh | bash

The Layers

Explore
the core loop

You keep the training loop. Tahuna handles the post-training infrastructure around it in four clear steps.

Tahuna init

tahuna init

Tahuna scans your project, detects your framework, identifies your entrypoint and data, and scaffolds anything missing.

Project-awareNo boilerplate

Align

tahuna sync

Your code and data are synced incrementally. Only changed files travel, and every run is pinned to exact snapshots.

Incremental syncDelta-only uploads

Train

tahuna train

Tahuna provisions the GPU, materializes the workspace, installs dependencies, and runs your training entrypoint.

Logs stream liveYour loop stays yours

Serve

tahuna serve

Tahuna provisions inference compute, loads a pinned model snapshot, installs what your service needs, and brings it online.

Inference-readyHealth-checked