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name: model-reference description: "Explains PyTorch-VAE model constructors, registry lookups, latent-shape conventions, and synthetic forward/loss/sample checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Model Reference

Use this sub-skill when the user wants to choose a model, inspect constructor arguments, compare variants, or run a tiny synthetic smoke on one architecture. It covers the shared registry, special kwargs, forward/loss outputs, and sample/generate caveats. The bundled command examples assume the generated skill directory is the current working directory.

Read first

• references/api-reference.md for constructor signatures and model-specific notes.
• references/troubleshooting.md for labels, sample availability, and pretrained-feature pitfalls.
• scripts/model_smoke.py for the bundled synthetic forward/loss/sampling helper.
• ../../references/model-overview.md when you only need a quick family map.

Include here

• Looking up a model name in models.vae_models.
• Understanding constructor kwargs such as latent_dim, num_samples, categorical_dim, embedding_dim, latent1_dim, and latent2_dim.
• Checking how a model's forward() result maps to loss_function() inputs.
• Knowing which models expect labels, multiple optimizers, or sample/generate flags.
• Running a tiny synthetic smoke against a selected model.

Exclude or route elsewhere

• Experiment orchestration, data_params, and checkpoint/logging setup -> sub-skills/training/SKILL.md.
• Full training jobs -> training.
• Repo provenance and staleness -> references/repo-provenance.md.

Typical triggers

• "what constructor does BetaTCVAE take"
• "how do I call sample on ConditionalVAE"
• "what kwargs does VQVAE need"
• "why does forward return extra tensors"
• "which models have generate()"
• "run a quick smoke for DFCVAE"

Workflow

1. Read the API reference to identify the exact class and kwargs.
2. Use the model smoke script with a small batch and the repo root you want to inspect.
3. If a model expects labels or a special optimizer index, follow the model-specific notes in the API reference before guessing.
4. If the model smoke fails because the model needs CUDA, network downloads, or a missing optional dependency, check the troubleshooting page before retrying.

Hand off to training when needed

If the user switches from "how do I instantiate this model" to "how do I run the experiment", route to the training sub-skill instead.