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name: forward-forward-training description: "Guides Forward-Forward training workflows, model-type selection, data loading, and Python compatibility troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

Forward-Forward training

Use this sub-skill when the user wants to train a Forward-Forward model, choose between the progressive/recurrent/NLP variants, or understand the package's source-era compatibility constraints.

Triggers

• Train a Forward-Forward model on MNIST or Aesop fables.
• Choose progressive, recurrent, or nlp model types.
• Handle predicted_tokens for the NLP variant.
• Debug Python-version issues or dataset-download behavior.

Read next

• references/api-reference.md for the public training API and model-type routing.
• references/data-and-compatibility.md for dataset loaders, vocabulary shape, and Python compatibility notes.
• references/workflows.md for the end-to-end training recipes.
• references/troubleshooting.md for import, download, and shape-failure recovery.
• scripts/forward_forward_probe.py for a safe import/signature check.

What to include

• The train_with_forward_forward_algorithm(...) entry point.
• The ForwardForwardModelType selector.
• MNIST and Aesop Fables data-loading behavior.
• The progressive, recurrent, and NLP training branches.
• The Python 3.9 compatibility constraint.

What to exclude

• Long training runs and dataset downloads as default actions.
• Repository-local notebook paths.
• Any unsupported model type beyond the three source-defined options.

Quick decision rule

If the user says “train the Forward-Forward model,” route here. If they say “how do I install the backend packages?” read the root install notes first, then return here for the workflow.