MoDa Community

What is ModelScope in the ModuBuild community?

ModelScope, part of the Moda community, is an open community for AI developers focused on models, datasets, and applications; it was established by Alibaba DAMO Academy. This platform brings together large language models, computer vision technologies, speech processing tools, multi-modal systems, as well as image, video, 3D, and scientific models. It offers model downloads, online inference capabilities, computing resources for Notebooks, creative workspaces, MCP services, SDKs, and community content.

MoDai is not a standalone chatbot, nor is it equivalent to Tongyi Qianwen. The Tongyi model is one of the key model providers in this community; the community also includes models from DeepSeek, Zhipu, Shusheng Puyu, Tencent Hunyuan, MiniMax, Kimi, Jieyue Xingchen, as well as numerous institutional and individual projects.

Model and Dataset Center

Users can search for models by task, framework, parameter scale, language, license, and popularity, and view model cards, files, versions, download counts, discussions, and online experiences. The dataset center provides data descriptions, file previews, versions, and usage instructions.

The fact that a model is included on the platform does not mean that ModelScope endorses its quality, security, or copyright. Before downloading, it is necessary to check the publisher’s credentials, the model details, the date of update, the license terms, any third-party underlying models used, and the required hardware.

Model download

ModelScope supports web downloads, Git, Python SDKs, and command-line tools. Developers can download complete snapshots or individual files, specifying the version, cache directory, and filtering rules.

The new ModelScope-Hub client also allows for the creation, uploading, downloading, and management of models, datasets, Studio, and MCP repositories.

Large models can contain dozens or hundreds of GBs of weights; before downloading them, it is necessary to assess the disk space, network connection, and Git LFS capabilities. When loading trust_remote_code or custom code, it is important to first examine the repository to avoid executing unknown files in an environment with high permissions.

ModelScope SDK

The official Python SDK provides functions for loading models, preprocessing data, performing inference using pipelines, training models, evaluating their performance, and accessing the Hub. Users can quickly run models for text, images, audio, etc., by using the task name and model ID; it also allows integration with PyTorch, Transformers, and other tools from the relevant ecosystem.

The SDK version may be incompatible with certain model examples; therefore, it is necessary to keep the versions of Python, CUDA, PyTorch, and ModelScope constant during installation. Class names or data interfaces mentioned in older tutorials might have changed, and in case of any issues, one should refer to the current model documentation and official guides.

Notebook and WebIDE

ModelScope Notebook is a cloud-based Linux environment for machine learning development; it allows Jupyter and WebIDE to run in a browser, and it offers GPU or CPU instances. Developers can open Notebooks directly from the model page to carry out tasks such as inference, fine-tuning, using ComfyUI, data processing, and application development.

The authorities used to provide free GPU credits as well as up to 100GB of persistent storage; the specific graphics cards available, the duration of use, waiting times, storage limits, and requirements for obtaining these resources vary depending on the campaign and the account in question. The free resources are not an unlimited service forever – idle instances may be stopped automatically.

Do not save plaintext keys or personal data in a Notebook. Before sharing it publicly, remove any output, environment variables, tokens, cache files, and data paths.

Incubation Space Studio

Incubation spaces are used to display and deploy AI applications, supporting methods such as Gradio, Streamlit, Docker, and static pages. Developers can publish chat functions, text-to-image generation, voice and video capabilities, as well as model demonstrations as accessible web pages, and they can also specify the CPU, memory, or GPU hardware to be used.

Community-provided free or event-based computing resources usually come with time limits, approval processes, and resource constraints, making them unsuitable as a formal production backend without an SLA. External services need to incorporate authentication, rate limiting, logging, content filtering, and cost control, in addition to having plans for migration to stable cloud resources.

Online reasoning API

Some models support direct online inference and can be invoked using ModelScope Tokens and compatible interfaces. The developers once offered a free daily invocation quota, but the list of available models, the number of invocations per day, the rate limits, and the business policies can change; therefore, the figure of 2000 invocations per day from those previous promotions should not be considered a permanent entitlement.

Different models vary in terms of whether they can be accessed via API, input constraints, concurrency limits, and pricing. When planning to use them for long-term production purposes, it is necessary to check the model’s page, the control panel, and the service terms to confirm that the model’s license permits their use in hosted services.

MCP ecosystem

ModuLink offers a MCP server marketplace as well as the official ModelScope MCP Server, which enables AI agents to search for models, datasets, applications, and papers using natural language, and to utilize functions such as image generation. The MCP repository can be deployed and managed through the official client.

MCP connects the Agent to external tools; before installation, it is necessary to check the publisher, tool permissions, environment variables, and source code. Do not allow unknown MCP instances to access local files, production databases, or long-term cloud keys.

Study Groups, Streams of Inspiration, and Communities

The Research Club is used to publish technical articles and tutorials; Inspiration Flow is intended for sharing Notebooks and practical examples; the discussion forum allows users to submit questions. The platform also organizes competitions, model evaluations, as well as courses and incentives for developers.

The AMD Joint Incentive Program for 2026 allows developers to earn GPU usage time through registration, articles, Notebooks, and creative spaces: registration grants 100 hours; each article provides 25 hours with a maximum of 50 hours per category; each Notebook yields 50 hours with a maximum of 150 hours per category; creative spaces can allocate 700 hours or more depending on the quality of the work submitted. The deadline for this program, the criteria for evaluation, and the list of compatible hardware are specified on the program’s official page, and these details do not represent fixed rights for regular accounts.

Model release and version management

Developers can create public, internal, or private repositories for models and datasets, upload model cards, weights, code, and version tags, and select licenses. Organizations can manage multiple projects in a centralized manner.

The uploader must possess the rights to distribute the model; they cannot re-license third-party models just because the platform allows the use of labels such as Apache or MIT. Derived models must retain the license terms of the original model, as well as the NOTICE and requirements regarding the training data, and they must also indicate any modifications made and the evaluations performed.

Prices and free resources

Web browsing, model cards, public repositories, and SDKs in the Magic Build community are available for free. Notebooks, GPUs, inference APIs, and creative spaces often offer basic free quotas, promotional rewards, or time-limited resources; however, the official website does not provide a single, permanent pricing table that applies to all services.

Excess computing power, stable deployment, and enterprise production needs may be covered by Alibaba Cloud PAI or through billing based on collaborative resources. This category should be labeled as “Free community + free quota + possible payment for cloud resources”; the actual price will be shown at the time of instance initiation or deployment.

GitHub and open-source projects

ModelScope has an official GitHub organization; both the core modelscope SDK and the new modelscope-hub client are licensed under the Apache 2.0 license. The official team also maintains:

• ModelScope Studio：AI application components based on Gradio and integrated with Ant Design;
• EvalScope：Large models, visual language, and AIGC evaluation frameworks;
• ModelScope MCP Server：The official MCP service for connecting to community resources;
• FunASR：Speech recognition toolchain;
• FunClip：Tools for speech transcription, subtitles, and video editing;
• ClearerVoice Studio：Speech enhancement, separation, and extraction of the target speaker;
• ms-swift：Tools for fine-tuning, inference, and deployment of large models and multimodal models.

The open-source nature of the core SDK does not mean that all models within the community are licensed under Apache 2.0. Model weights may be licensed under MIT, Apache, research licenses, non-commercial licenses, or custom community agreements; datasets also come with their own usage conditions.

Security and supply chain risks

A model repository may contain pickle files, Python scripts, Dockerfiles, and commands for installing dependencies. Malicious or compromised repositories can execute arbitrary code.

The submission version should be fixed, the files should be checked, and the code should be run within a container; in addition, it is necessary to avoid mounting host credentials into it.

The output of open models may contain biases, errors, and illegal content. Deployers are responsible for carrying out security tests, content audits, as well as ensuring privacy and compliance; an online demonstration on the platform does not equate to certification of safety for production use.

Guide to Using ModelScope in the Magic Build Community

Complete a basic task.

1. Register in the ModelScope community of ModuLink and create an API Key intended solely for use in a testing environment;
2. Select a model based on input type, context, quality, speed, and price;
3. First, call the model and dataset center to submit the minimum request and check the returned structure;
4. Then use the model to download and test the streaming output, parameters, and abnormal response;
5. Record Tokens, number of calls, latency, error rate, and cost per call;
6. Move the key to the server-side key manager before integrating it into the actual application;

Create reusable professional workflows

1. Different keys and quotas are used for development, testing, and production environments;
2. Representative evaluation sets are created based on the model and dataset centers, model downloads, and the ModelScope SDK;
3. Set timeout, concurrency, retry, throttling, and budget limits;
4. Perform checks on the output regarding facts, security, format, and sensitive information;
5. Monitor changes in model version, price, latency, and failure rate;
6. Prepare plans for downgrading the model, implementing circuit breaking, and taking manual control;

Which users is it suitable for?

• Developers who search for and download open-source models from home and abroad;
• Users who need Chinese documents, a domestic network, and the ModelScope format;
• Students who do not have a local GPU and wish to use a notebook to work with models;
• Authors who publish models as Gradio, Streamlit, or Docker demonstrations;
• Researchers who share datasets, Notebooks, tutorials, and evaluation results;
• Teams that integrate community resources into the Agent through SDKs, APIs, or MCP.

Usage restrictions and precautions

• Free GPUs may be on a waiting list, available for a limited time or in a different model, and innovation hubs may also go into dormant mode;
• When training or deploying long-running tasks, it is necessary to save checkpoints and logs, and one should not rely on temporary sessions.
• The number of model downloads and likes do not indicate accuracy, security, or commercial licensing;
• When making a selection, it is necessary to use one’s own data for evaluation, and to check the origin of the model, the date of training, the context, the hardware used, as well as the license.

Frequently Asked Questions

Is the MoDai community free?

Model browsing, public model downloads, and the SDK are available for free; Notebook, GPU, online inference, and deployment come with free quotas or promotional resources, though the specific duration and any additional costs may vary.

Is MoDai the same as Tongyi Qianwen?

They are not the same. Modo is a community for models and applications, while Tongyi Qianwen is Alibaba’s family of models – it is just one of the important elements within that community.

Is it possible to download the model for local deployment?

Yes. Downloads are available via web page, SDK, command line, and Git, but it is necessary to verify the hardware, model card, and license.

Are free GPUs available?

Notebooks in basic or interactive formats, as well as GPU resources in innovation spaces are available; the requirements for obtaining them, along with the specifications of the graphics cards and the duration of use, are specified on the account page and in the rules of the respective campaigns.

Are APIs provided?

Some models offer online inference APIs and can also be deployed on one’s own. The list of models, the number of free uses, the rate, and the price need to be checked in real time.

Is Magic Build open source?

The core SDK, Hub client, Studio, MCP, and other official projects are available under an open-source license; for models, datasets, and applications within the community, their respective licenses must be checked separately.