---
id: modelscope
version: "1.39.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# modelscope — ModelScope: bring the notion of Model-as-a-Service to life.

License: permissive · Maintenance: active · Downloads: 5.4M/mo

## What it is and what it does

ModelScope is a Model-as-a-Service library that unifies access to hundreds of pretrained machine learning models across computer vision, NLP, speech, multi-modal, and scientific computing. It abstracts away model loading and inference complexity behind a simple pipeline interface, allowing developers to perform inference, fine-tuning, and evaluation with minimal code. The library manages interactions with ModelScope's backend services for model discovery, version control, and cache management.

The package integrates with a hub of 700+ models covering state-of-the-art implementations in domains like image matting, text segmentation, speech recognition, and large language models. Developers can download models by name, run inference on various input types (images, text, audio, video), and customize components as needed. It depends on standard utilities like requests, tqdm, filelock, and packaging for model management and HTTP operations.

Use it for:

- Run inference on pretrained NLP models for tasks like word segmentation, text classification, or named entity recognition without manual model loading.
- Perform computer vision tasks such as portrait matting, face detection, or text recognition by calling a pipeline with an image URL or file path.
- Fine-tune a pretrained model on a custom dataset using the Trainer interface for domain-specific adaptation.
- Access and experiment with large language models and multi-modal models hosted on ModelScope for prototyping.
- Build production inference pipelines that automatically download and cache models from the ModelScope hub.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

ModelScope provides unified Python interfaces for inference, fine-tuning, and evaluation across machine learning models spanning NLP, computer vision, speech, multi-modal, and scientific computing domains.

Yes. ModelScope is actively maintained, has low install friction, carries no known vulnerabilities, and offers permissive licensing. It is well-suited for developers who want to quickly prototype or deploy inference using pretrained models across multiple AI domains without building model loading and management infrastructure from scratch.

## Install

pip install modelscope
uv add modelscope
poetry add modelscope

## Installing modelscope

Before you install: Low install friction with a pure-Python wheel distribution. Active maintenance with a recent release (10 days old) and 9088 repository stars. Requires Python 3.10 or later.

License in practice: Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions beyond attribution and liability disclaimers.

Quickstart:

pip install modelscope

from modelscope.pipelines import pipeline
word_seg = pipeline('word-segmentation', model='damo/nlp_structbert_word-segmentation_chinese-base')
result = word_seg('今天天气不错')
print(result)

Requires Python 3.10 or later; models are downloaded on first use and may require significant disk space depending on model size.

Verify before relying:

- Whether model downloads are cached locally and how much disk space typical models consume.
- GPU support and whether CUDA/PyTorch dependencies are automatically installed or must be configured separately.
- Whether the library supports model serving or only local inference.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 5.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

model inference pipeline, fine-tuning framework, multi-modal model hub, nlp cv speech models, model-as-a-service library, unified ml model interface, pretrained model download, model-hub, inference-pipeline, multi-domain-ml

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