{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"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.","skillfed_tags":["model-hub","inference-pipeline","multi-domain-ml"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"modelscope","links":{"html":"https://skillfed.io/packages/modelscope","md":"https://skillfed.io/packages/modelscope.md","pypi":"https://pypi.org/project/modelscope/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-04","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"modelscope","python_support":"supports_current","summary":"ModelScope: bring the notion of Model-as-a-Service to life."},"popularity":{"monthly_downloads":5364125,"position":2112,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.39.1"}