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ModelScope Cookbook

ModelScope Cookbook is an open-source, hands-on guide to applying open-source AI models, covering model selection, inference, data preparation, fine-tuning, evaluation and application development, including RAG and agent workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

ModelScope Cookbook — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when planning a practical open-source model experiment, learning a particular stage of the model workflow, or tracing a path from initial inference toward an AI application.

Who it is for

  • Developers and students learning to build with open-source AI models.
  • Application engineers making model, compute, fine-tuning or evaluation decisions.
  • Generative-AI practitioners exploring image customization, generation and editing.

Documented uses

  • Translate a business problem into a model task with suitable inputs, outputs and evaluation criteria.
  • Compare candidate models and plan for hardware and resource requirements.
  • Run an initial inference and explore local or cloud execution approaches.
  • Prepare data, fine-tune a model and evaluate it against a baseline.
  • Study application patterns for RAG, agents and generative-AI use cases.
Before you choose

Requirements & limits

Online reading is available through ModelScope; local reading is documented with Python 3 and a modern browser. The repository is licensed under Apache-2.0.

  • The cookbook is learning material, not a hosted inference service; model execution takes place through chapter-specific exercises.
  • The online reader includes search, guided paths, chapter navigation, code copying, image enlargement and rendered formulas.
  • The README and chapter titles have different language coverage: the README is available in English and Simplified Chinese, and chapter titles are in Chinese.
  • Basic Python and command-line familiarity are useful for coding exercises; individual chapters specify any additional software, credentials, downloads or compute needs.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Read the cookbook locally

Serve the repository’s reading files on your machine for browser access.

Before you start

  • A local copy of the ms-cookbook repository
  • Python 3
  • A modern browser

Steps

  1. Open a terminal in the repository directory.

  2. Run the command below.

    Documented command
    python3 -m http.server 4173 --bind 127.0.0.1

  3. Open http://127.0.0.1:4173/#home in a browser; if port 4173 is occupied, use 4174 in both the command and URL.

  4. Stop the server with Ctrl+C when finished.

Expected outcome

The cookbook can be read locally in a browser.

Guide limitations

  • This procedure serves the reading files; it does not run the model exercises.
  • Individual exercises may require model downloads, credentials, software or compute resources as described in their chapters.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
modelscope/ms-cookbook · Official homepage title · retained in its source language
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