The outcome

Get into the official ModelScope experience with the right account and a setup you understand.

ModelScope is a Open AI model, dataset, Studio, and developer ecosystem with hub, Python frameworks, deployment, and MCP resources. Discovering, evaluating, downloading, training, and publishing open models and datasets, or deploying controlled Studio and MCP projects through one AI asset hub. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Start at https://modelscope.cn/ and confirm the domain before entering account or payment information.

  2. 02

    For desktop use, follow the official download link. Current availability: ModelScope is available through its web hub plus the modelscope and modelscope-hub Python packages and ms-hub command-line interface..

  3. 03

    Python 3.10 or later for the current hub client, sufficient local or cloud compute and storage, a ModelScope token for private or publishing operations, verified asset licenses and revisions, and a sandboxed evaluation environment.

  4. 04

    Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.

  5. 05

    Run one low-risk test task before connecting sensitive files, repositories, or workspace data.

Working standard

What good use looks like.

  • Use official download pages.
  • Review permissions during setup.
  • Keep installers and applications updated.

A hub listing is not an independent security, quality, or licensing review. Model repositories, datasets, notebooks, serialized weights, dependencies, Studio apps, MCP servers, and custom loading code can execute code or expose data. Pin revisions, inspect cards and licenses, verify hashes, isolate execution, avoid secrets and sensitive data, limit tokens, and preserve provenance and deletion controls.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.