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name: model-library description: "Discover timm model names, instantiate models, load pretrained or local weights, adapt heads/input channels/pooling, inspect pretrained configs, extract features, and debug model registry or weight-loading issues." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Model Library

Use this sub-skill when a task asks an agent to choose or instantiate a timm architecture, enumerate available models or pretrained tags, load weights, adapt classification heads or input channels, inspect model defaults, extract feature maps, or troubleshoot model creation and pretrained loading.

Fast Path

1. Import from the public package surface: import timm, then use timm.list_models, timm.list_pretrained, timm.get_pretrained_cfg, and timm.create_model.
2. Prefer no-download checks while iterating: timm.create_model(name, pretrained=False) and run a small random forward pass.
3. Use list_models(filter='*resnet*') for architecture discovery and list_pretrained(filter='*resnet*') for exact architecture.tag pretrained identifiers.
4. Create a model with the exact signature from timm 1.0.28.dev0: create_model(model_name, pretrained=False, pretrained_cfg=None, pretrained_cfg_overlay=None, checkpoint_path=None, cache_dir=None, scriptable=None, exportable=None, no_jit=None, **kwargs).
5. Adapt common dimensions at construction time: num_classes, in_chans, global_pool, features_only, out_indices, output_stride, scriptable, exportable, and no_jit.
6. Inspect model.pretrained_cfg or timm.get_pretrained_cfg(model_name).to_dict() before choosing transforms, class counts, input channels, or cache/download behavior.

Decision Guide

• Need model discovery or exact API signatures: read references/api-reference.md.
• Need classifier/input/pooling/feature-extraction recipes: read references/model-workflows.md.
• Need to diagnose unknown names, tag failures, cache/auth/download problems, checkpoint mismatches, or feature shape surprises: read references/troubleshooting.md.
• Need a safe local smoke test without network access: run python sub-skills/model-library/scripts/model_smoke_check.py --model resnet18 --num-classes 7 from the skill root or copy the script into a project.

Boundary Routing

• Use ../data-pipelines/ for transforms, loaders, resolve_data_config, augmentation, and preprocessing pipelines after reading pretrained_cfg here.
• Use ../training-workflows/ for optimizers, schedulers, losses, training loops, fine-tuning scripts, and distributed training.
• Use ../export-and-interoperability/ for ONNX, TorchScript export flows, checkpoint conversion tools, Hugging Face push/pull packaging, and deployment formats.
• Use ../layers-and-components/ for low-level timm layers, blocks, activations, pooling modules, and component internals.

Guardrails

• Do not set pretrained=True in smoke checks unless downloads, cache location, and Hugging Face authentication are expected and acceptable.
• Do not assume all names returned by list_models() have pretrained weights; use list_pretrained() or is_model_pretrained() when weights matter.
• Do not hardcode image normalization, crop size, or class count for pretrained models; inspect pretrained_cfg and route preprocessing details to data-pipelines.
• Do not assume features_only=True returns the same number of feature levels for every model family; inspect model.feature_info.channels() and model.feature_info.reduction().