ModelScope Open Source Models

ModelScope is an open-source model community and AI innovation platform launched jointly by Alibaba Intelligent Computing Research Institute and CCF Open Source Development Committee in June 2022. It adheres to the Model-as-a-Service concept and unites top global institutions and developers to build an open and thriving open-source AI ecosystem. The platform provides one-stop full-process services covering model exploration, development, fine-tuning, evaluation and deployment, ranking among China’s most active and full-modal open-source AI communities.

I. Core Functions

Centering on the full lifecycle of AI development, ModelScope has built a complete product system with the following core capabilities:

1. Massive ModelHub Library

It hosts thousands of AI models contributed by top global institutions and developers covering all modalities and tasks with frequent updates. As of May 2026, it has launched cutting-edge models such as Ling-2.6-1T, Kimi-K2.6 and DeepSeek-V4-Flash.

• Computer Vision: Visual detection & tracking, OCR, face & human body analysis, image classification, visual editing, image segmentation, etc.
• Natural Language Processing (NLP): Text classification, text generation, word segmentation, named entity recognition, machine translation, text summarization, etc.
• Speech Technology: Speech recognition, inverse text normalization, voice synthesis, voice wake-up, noise reduction and echo cancellation, etc.
• Multimodal: Image captioning, video description, visual grounding, text-to-image, text-to-video, multimodal representation learning, etc.
• Scientific Computing: Frontier research fields including protein structure generation and protein function prediction.

2. Professional Dataset Center

It offers abundant open-source AI datasets to meet training and evaluation demands across all modalities, including high-quality resources like MegaStyle-1.4M, OmniAction and LingBot-Depth-Dataset, supporting one-click download and direct usage.

3. AI Application Studio

A free and flexible platform for AI application development and demonstration. Users can rapidly build and showcase various AI applications based on atomic model capabilities. Popular studio projects such as IndexTTS-2-Demo, Qwen3-VL-Demo and Wan2.2-Animate are available, and users are allowed to create and share customized applications.

4. Full-Stack Development Toolchain

• ModelScope Library: Unified Python library enabling efficient model inference, fine-tuning and evaluation, as well as cross-modal unified calling, greatly lowering AI development barriers.
• EvalScope: Dedicated high-performance framework for large model evaluation and benchmark testing, supporting customized metrics and datasets to verify model performance efficiently.
• Swift: Training and inference toolkit compatible with mainstream models including LLaMA, Qwen, ChatGLM and Baichuan, supporting LoRA, ResTuning, NEFTune and other efficient tuning methods.
• ModelScope-Agent: Agent development framework that integrates diverse model capabilities on the platform to build complex AI agent applications quickly.

II. Free Access Policy

The core functions and most resources of ModelScope are fully open-source and free:

• All open-source models, datasets and development tools are free for personal and non-commercial use.
• Free basic computing resources are provided for online model trial, simple inference and fine-tuning tasks.
• The Studio application building zone is completely open for free.
• Customized commercial services and large-scale computing resources are available at charges for enterprise users.

III. Quick Start Guide

1. Online Trial (No Installation Needed)

Directly visit the official website https://modelscope.cn, select preferred models or applications in Models or Studios sections to experience functions instantly without local environment configuration.

2. Local Development via Python Library

1. Environment Preparation: Install Python 3.8 or above.
2. Install ModelScope Library

   pip install modelscope
   

3. Model Calling: Take text generation as an example:

   from modelscope import AutoModelForCausalLM, AutoTokenizer
   model = AutoModelForCausalLM.from_pretrained("qwen/Qwen3.6-27B", device_map="auto")
   tokenizer = AutoTokenizer.from_pretrained("qwen/Qwen3.6-27B")
   inputs = tokenizer("Hello, ModelScope!", return_tensors="pt").to(model.device)
   outputs = model.generate(**inputs, max_new_tokens=100)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   

3. Model Fine-tuning & Evaluation

Use Swift for efficient large model fine-tuning, or adopt EvalScope to conduct comprehensive performance assessment. Detailed documents and sample codes are officially provided.

4. Build & Publish AI Applications

Create new projects in Studios, develop customized AI applications rapidly with official templates and model capabilities, and publish them with one click for sharing.

IV. Target Users

• AI Developers: Individual developers, startup teams and enterprise technicians to acquire model resources and cut development costs.
• Researchers: Scholars from universities and research institutes for academic studies using cutting-edge models and datasets.
• Students: AI majors to learn technologies and practice model development and deployment.
• Product Managers & Entrepreneurs: Quickly verify AI product prototypes and shorten time-to-market cycle.
• Enterprise Decision-makers: Keep track of AI trends and assess industrial application potential.

V. Application Scenarios

ModelScope supports full-scenario AI deployment, including typical use cases as follows:

• Content Creation: Text generation, text-to-image, text-to-video, voice synthesis, etc.
• Intelligent Customer Service: LLM-based Q&A and intent recognition.
• Industrial Quality Inspection: Product defect detection via computer vision.
• Security Monitoring: Face recognition, behavior analysis and object tracking.
• Medical & Healthcare: Medical image analysis and protein structure prediction.
• EdTech: Intelligent tutoring, content generation and oral assessment.
• Scientific Research: Large model training, multimodal research and scientific computation.
• Enterprise Digitalization: Document processing, data annotation and intelligent office work.

VI. Core Competitive Advantages

1. Full-modal Coverage & Timely Updates: One of the few domestic platforms covering CV, NLP, speech, multimodality and scientific computing. New top-tier models are launched weekly to keep pace with AI frontiers.
2. All-in-one Development Experience: Integrates models, datasets, toolkits, computing resources and demo platforms to realize end-to-end AI development without cross-platform operations.
3. User-friendly Toolchain: Unified Python interfaces simplify inference and fine-tuning; EvalScope and Swift greatly lower technical barriers for model training and evaluation.
4. Open Ecosystem & Active Community: Initiated by Alibaba and CCF, it gathers massive global contributors with rapid community response.
5. China-friendly Chinese Ecosystem: Abundant native Chinese models and localized documents better fit domestic development demands and Chinese scenario optimization.
6. Abundant Free Resources: Sufficient free computing power and open-source resources reduce entry thresholds for individual developers and SMEs.

VII. Practical Evaluation

User experience based on the latest version updated in May 2026:

• Model Richness: ★★★★★ Covers nearly all mainstream AI tasks with extremely fast iteration speed.
• Ease of Use: ★★★★☆ Concise unified APIs and zero-config online trials suit beginners; certain advanced functions require solid technical foundations.
• Performance: ★★★★☆ Optimized models deliver fast inference speed; free resources satisfy learning and small projects while paid resources are recommended for large-scale tasks.
• Community Support: ★★★★☆ Complete official documents and active forums ensure efficient problem solving; regular AI training camps and tech sharing are held offline and online.
• Application Ecosystem: ★★★★☆ A large number of mature AI demos are available in Studios, with the ecosystem expanding rapidly.

VIII. Usage Notes

1. Diversified Model Licenses: Different models adopt various open-source licenses such as Apache 2.0, MIT and GPL. Check license terms carefully before commercial usage.
2. Computing Resource Limits: Free resources have quota and duration restrictions. Upgrade to paid services or use local devices for large-scale training and inference.
3. Data Security & Privacy: Avoid uploading confidential business data or personal sensitive information for platform processing.
4. Version Compatibility: Version conflicts may exist between ModelScope library and models. Follow official recommended version combinations.
5. Adequate Performance Verification: Conduct sufficient field tests before formal deployment since model performance varies in different scenarios.

IX. FAQ

Q1: Are ModelScope models available for commercial use?

A: Most open-source models allow commercial application, while some have non-commercial restrictions or require official authorization. Confirm license information on model detail pages or consult authors in advance.

Q2: How much free computing resource is provided?

A: Free CPU and GPU computing time is offered for individual users with adjustable quotas based on platform policies. Users can check resource usage in personal centers.

Q3: How to resolve dependency errors during model calling?

A: Adopt officially recommended Python and dependency versions or build isolated virtual environments via Conda. Refer to official configuration guides or seek help in community forums.

Q4: Can users upload self-trained models to ModelScope?

A: Yes. Developers are welcome to contribute models and datasets. Submit works in ModelHub for official review before public display and sharing.

Q5: Ways to get technical support?

A: Users can get assistance via the following approaches:

• Browse official documents and tutorials
• Post questions in ModelScope community forums
• Join official technical communication groups
• Submit issues on GitHub