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model-io-and-hub【数据与研究】Save, load, inspect, minify, and share scvi-tools model artifacts locally or through Hugging Face Hub metadata.
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Use this sub-skill when the task is about scvi-tools model persistence: model.save(), ModelClass.load(), saved registry inspection, matching an AnnData/MuData to a saved model, minified data artifacts, scvi.hub.HubModel, Hub metadata/model cards, or version compatibility checks.
Do not use this sub-skill for choosing model classes, training schedules, callbacks, GPU strategy, inference outputs, or general installation. Route those to the root skill, data setup, core model, multimodal/spatial, and training/inference sub-skills as appropriate.
model.save(dir_path, overwrite=False, save_anndata=False, prefix=None); use save_anndata=True only when the training data is small and shareable.scvi.model.SCVI.load(dir_path, adata=adata, accelerator="auto", device="auto", prefix=None).adata is omitted, scvi-tools tries to load adata.h5ad or mdata.h5mu saved next to model.pt; if none exists, the model loads without data only when the workflow does not immediately need data-bound methods.ModelClass.view_setup_args(dir_path), ModelClass.load_registry(dir_path), and scripts/check_saved_model.py.HubMetadata and HubModelCardHelper first, instantiate HubModel(local_dir, metadata=..., model_card=...), call hub_model.save(overwrite=True), and only then push when credentials/network are available.HubMetadata, HubModelCardHelper, HubModel, Hugging Face/S3 flows, and credential caveats.scripts/check_saved_model.py --help to inspect a saved local model directory without importing private repository paths.