---

name: modelscope description: "Use ModelScope SDK workflows for Hub access, pipelines, datasets, training, serving, export, customization, and repository contribution without relying on a source checkout." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

ModelScope Repo Skill

Use this skill when a task names ModelScope or needs a Python SDK for Model-as-a-Service workflows: Hub downloads and cache management, model inference pipelines, dataset/config loading, training/evaluation planning, serving/export utilities, custom pipeline extension, or focused repository-development guidance.

This skill is self-contained for future Researcher use. It distills package behavior, safe command shapes, helper scripts, and troubleshooting notes; do not reopen the original source checkout for routine use.

Install and quick check

Choose the smallest extra set for the task:

pip install modelscope
pip install 'modelscope[framework]'
pip install 'modelscope[server]'

Domain extras such as cv, nlp, audio, multi-modal, and science can be large and backend-specific. Install them only when the selected model/task requires them. For CPU-only planning, pass device='cpu' to pipeline(...) and use local/offline smoke checks first.

Run the bundled environment checker after installation:

python scripts/check_modelscope_environment.py --summary

The checker imports public modules, reports package versions, verifies CLI help when available, and can optionally probe torch/CUDA. It does not download models, contact the Hub, start a server, train, or mutate files.

Route by user request

User intent	Read
Download or upload Hub repositories/files, inspect/cache/clear ModelScope cache, authenticate, set endpoint/cache variables, plan offline downloads, or use modelscope/ms CLI.	hub-and-cli
Build inference with pipeline(...), load Model.from_pretrained(...), inspect task/output keys, use local model configs, debug registries, devices, batching, plugins, or trust_remote_code.	pipelines-and-models
Load local/HF/ModelScope datasets, validate dataset recipes, use JSON/YAML file IO, parse/merge Config, or handle .py config trust gates.	datasets-config
Plan fine-tuning/evaluation, convert TrainingArgs flags to config, use build_trainer, choose checkpoint/hooks/metrics, or preflight model/data/GPU requirements.	training-and-evaluation
Launch ModelScope server, choose vLLM handoff, inspect exporter support, or dry-run checkpoint conversion/weight-diff utility side effects.	serving-export-and-tools
Create custom pipeline/model/preprocessor wrappers, scaffold template commands, register components, understand plugins/lazy imports, or contribute to ModelScope source.	customization-and-development

Repo-level references

• Read package overview for package surfaces, optional extras, public entry points, and environment choices.
• Read troubleshooting for install/import, optional dependency, backend, cache, trust, and workflow-selection failures that cut across sub-skills.
• Read repository provenance before deciding whether this skill is stale for a newer checkout.
• references/repo-routing-metadata.json is structured router metadata for managed repo-skill import; it is not a user manual.

Operating guardrails

1. ModelScope can download models/datasets and run remote-code/plugin paths. Confirm trust, credentials, endpoint, cache root, and network policy before executing Hub/model-loading commands.
2. Many domain examples require large model/data downloads, GPU memory, optional extras, or credentials. This skill verifies package-level CPU/base workflows; CUDA/domain execution is optional and must be verified in the target environment before claiming success.
3. Prefer dry-run planners and safe helper scripts before side-effecting commands: hub-and-cli/scripts/plan_download.py, training-and-evaluation/scripts/build_training_args_preview.py, serving-export-and-tools/scripts/checkpoint_conversion_plan.py, and customization-and-development/scripts/pipeline_template_plan.py.
4. Do not treat trust_remote_code=True, Python configs, plugins, uploads, cache clears, real training, server launch, or checkpoint conversion as harmless. Require explicit user approval or a clear task mandate.
5. When a workflow spans multiple surfaces, route in order: Hub/cache → dataset/config → pipeline/training/serving → troubleshooting.

Minimal API memory

from modelscope.pipelines import pipeline
from modelscope.msdatasets import MsDataset
from modelscope.trainers import build_trainer
from modelscope.hub.snapshot_download import snapshot_download

model_dir = snapshot_download('owner/model', local_files_only=True)
pipe = pipeline(task='text-classification', model=model_dir, device='cpu')
# dataset = MsDataset.load('csv', data_files={'train': 'train.csv'}, split='train')
# trainer = build_trainer(name='trainer', default_args={'model': model_dir})

The exact task, model, dataset, trainer name, extras, and backend requirements come from the selected sub-skill and user context.