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name: training description: "Routes textgenrnn training, train_on_texts, train_new_model, train_from_file, and train_from_largetext_file workflows for fine-tuning, context labels, word-level inputs, and scratch architectures." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

training

Use this sub-skill when the task is to train, fine-tune, resume, or debug a textgenrnn model. It covers in-memory text lists, newline-delimited files, CSV-backed datasets, context labels, word-level inputs, and single large text blocks.

Use this route for

• Fine-tuning the bundled pretrained textgenrnn model on a small or medium text corpus.
• Training a fresh scratch model with custom word_level, rnn_layers, rnn_size, rnn_bidirectional, max_length, max_words, and dim_embeddings settings.
• Training from a newline-delimited file, a one-column CSV, or a two-column context CSV.
• Training on a single large text block by treating the file as one document.
• Managing train_size, validation, dropout, save_epochs, and optional multi_gpu acceleration.
• Verifying the saved config, vocab, and weights artifacts after a scratch training run.

Do not use this route for

• Generation-only sampling, prefix completion, or interactive text creation. Route to ../generation/SKILL.md.
• Embedding vectors, similarity search, PCA, or t-SNE analysis. Route to ../embedding-analysis/SKILL.md.
• Import, TensorFlow, Keras, or setuptools compatibility failures. Use ../../references/installation-and-compatibility.md first.

Read first

• references/api-reference.md for the verified training signatures, defaults, and artifact names.
• references/data-formats.md for newline files, one-column CSV, two-column context CSV, single-large-text, and word-level data choices.
• references/workflows.md for common training and fine-tuning recipes.
• references/troubleshooting.md for batch-size, CSV, context, overfitting, and save-path failures.
• ../../references/model-overview.md for architecture and config background.
• ../../references/installation-and-compatibility.md when imports or backend setup fail.

Skill-owned script

• scripts/smoke_train_tiny.py — creates a temporary tiny fixture, trains a scratch model for one epoch, and asserts that the config, vocab, and weights files were written.

Operational checklist

1. Decide whether the request is fine-tuning or scratch training.
   • Use train_on_texts to continue from the current model.
   • Use train_new_model when you want a fresh vocabulary, a fresh config, or a word-level model.
2. Decide where the data comes from.
   • Use an in-memory list when the caller already has texts.
   • Use train_from_file for newline files, CSV files, or context-labeled CSV files.
   • Use train_from_largetext_file when the source file is one large document.
3. Decide whether labels are part of the dataset.
   • Pass context_labels=[...] with the same length as texts for in-memory contextual training.
   • Pass context=True for a two-column CSV where column 1 is text and column 2 is label.
4. Decide how much overfitting control you need.
   • Use train_size < 1.0 to hold out validation sequences.
   • Keep validation=True if you want validation loss.
   • Use dropout sparingly; high values can hurt convergence.
5. Decide whether the run should stay tiny or produce samples.
   • Set gen_epochs=0 and validation=False for smoke tests and artifact checks.
   • Increase num_epochs, train_size, or corpus size when you want real quality.

File-format reminders

• train_from_file(..., header=True, delim="\n", is_csv=False) skips the first row by default.
• Set is_csv=True for a one-column CSV that stores texts in the first column.
• Set context=True for a two-column CSV with text in the first column and the context label in the second.
• train_from_largetext_file reads the entire file into a single-element text list and sets single_text=True.
• For word-level training, smaller max_length values usually work better than the default character-level setting.

Output expectations

• Scratch training writes name_config.json, name_vocab.json, and name_weights.hdf5.
• If save_epochs > 0, intermediate snapshots are written as name_weights_epoch_<epoch>.hdf5.
• save() writes only the current weights file; it does not rewrite the config or vocab files.
• load() expects a matching weights file for the current config and vocabulary shape.

Routing notes

• If the caller only needs to see generated samples after training, hand off to generation.
• If the caller is measuring semantic clustering or similarity, hand off to embedding-analysis.
• If the issue is an import mismatch or a bad TensorFlow stack, stop here and use the compatibility reference.
• If the user wants a lower-level model architecture explanation, use the root model overview instead.