Install the MinT SDK

The MinT client SDK ships as mindlab-toolkit — a pure Python package. Enterprise and Community share the same package, so code migrates at zero cost; the only differences are the endpoint and the API key.

Requirements

• Python 3.11 or above.
• We recommend creating an isolated environment with venv or conda.
• No local GPU needed — the client only assembles training requests; all forward / backward / sampling runs on the MinT cluster.
• Network access to your enterprise tenant's MinT endpoint (mint.macaron.xin or mint-cn.macaron.xin, or your private cluster address).

Install command

pip install git+https://github.com/MindLab-Research/mindlab-toolkit.git

The install also pulls in the mint package along with the compatible tinker (>= 0.15.0) runtime helper library.

Verify

python -c "import mint; print(mint.__version__)"

tinker-compatible entry point

If you already have a Tinker training script, migrating to MinT takes just one import line change:

import mint as tinker

When you import mint, it automatically patches tinker's key-validation logic so that sk-* API keys work directly.

Namespaces

The core namespaces available after install:

• mint: the core API (ServiceClient, create_lora_training_client, forward_backward, optim_step, save_weights_and_get_sampling_client, etc.).
• mint.types: type definitions (Datum, ModelInput, AdamParams, SamplingParams, etc.).
• mintx: MinT-specific extensions (including constants like mintx.OPENPI_FAST_MODEL).

Offline install for private environments

Private delivery explicitly includes "dependency images and offline install packages, supporting one-click startup in a fully air-gapped environment." For Enterprise private-deployment customers, the Mind Lab team delivers the offline install package and upgrade process together with the cluster during onboarding — no need to build your own pip mirror.