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name: generation description: "Guides agents through textgenrnn generate, generate_samples, generate_to_file, save/load, prefix, temperature, interactive, and synthesize workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

textgenrnn generation sub-skill

Use this sub-skill when the user wants to run, inspect, or troubleshoot text generation with textgenrnn models. It covers default bundled weights, custom weight/vocabulary/config triplets, sampling controls, interactive next-token selection, writing generated text to files, weight save/load, and multi-model synthesis.

Do not reopen the source repository or notebooks for these tasks. Use this router plus the bundled references and helper script.

Trigger tasks

Activate this sub-skill for requests that mention any of the following with textgenrnn:

• instantiate textgenrnn() or load pretrained/default weights;
• call generate, generate_samples, or generate_to_file;
• choose prefix, temperature, temperature cycles, or max_gen_length;
• use interactive=True, top_n, or terminal next-character controls;
• save or reload HDF5 weights with save or load;
• load custom weights_path, vocab_path, and config_path files;
• combine several trained models with synthesize or synthesize_to_file;
• smoke-test generation or create a small generated text file.

Boundaries and routing

Stay in this sub-skill for generation-time behavior only.

• For model training, fine-tuning, context labels, word-level training, train_on_texts, train_new_model, train_from_file, or train_from_largetext_file, route to training.
• For encode_text_vectors, PCA/t-SNE vectors, and similarity, route to embedding-analysis.
• For import failures, TensorFlow/Keras compatibility, or pkg_resources problems, consult the root installation and compatibility guide.
• For architecture and config background, consult the root model overview.

Runtime assumptions

• Public import: from textgenrnn import textgenrnn.
• Public distribution: textgenrnn==2.0.0.
• Use a pre-Keras-3 TensorFlow stack, such as TensorFlow/Keras 2.15.x, and ensure pkg_resources is available through a compatible setuptools install.
• CPU generation is valid. CUDA/GPU is optional acceleration for training, not a requirement for generation covered here.
• The repository provides no CLI; use Python APIs or the bundled helper script.
• Notebook examples have already been distilled into this skill. Do not ask a future agent to open notebooks for normal generation work.

Primary references

• API reference lists verified signatures, defaults, return values, and parameter caveats.
• Workflows gives copy-ready recipes for common generation tasks.
• Troubleshooting maps symptoms to fixes.
• smoke_generate.py is a safe argparse helper for short generation checks and output-file validation.

Standard generation workflow

1. Confirm whether the user wants the bundled default model or a custom model.
2. If using a custom model trained from scratch, require the matching triplet: weights HDF5, vocabulary JSON, and config JSON.
3. Instantiate the model with textgenrnn(...).
4. For programmatic use, call generate(..., return_as_list=True) so generated strings are returned instead of only printed.
5. For a file artifact, use either generate_to_file(destination_path, ...) or scripts/smoke_generate.py --output-file ....
6. Keep max_gen_length small for smoke tests and increase it only when the user asks for longer outputs.
7. If generation fails before sampling, check import/runtime compatibility and matching model files before changing sampling parameters.

Minimal API example:

from textgenrnn import textgenrnn

textgen = textgenrnn()
texts = textgen.generate(n=3, return_as_list=True, max_gen_length=80,
                         progress=False)

Custom model example:

from textgenrnn import textgenrnn

textgen = textgenrnn(weights_path="my_model_weights.hdf5",
                     vocab_path="my_model_vocab.json",
                     config_path="my_model_config.json",
                     name="my_model")
texts = textgen.generate(n=2, prefix="Once", temperature=[0.2, 1.0],
                         max_gen_length=120, return_as_list=True,
                         progress=False)

Sampling controls

• temperature may be one float or a list of floats. A list cycles across generated tokens, which can mix conservative and creative sampling.
• Lower temperatures are more repetitive and stable; higher temperatures are more varied and can become incoherent.
• prefix seeds the generated text. Characters or words not in the model vocabulary may be encoded as unknowns, so a prefix cannot guarantee quality.
• max_gen_length limits generated token length, not training context length; model context length comes from the config max_length.
• Set progress=False for automation to avoid progress bars.

Interactive generation

Use generate(interactive=True, top_n=N) only in a real terminal with stdin. The model prints the top N next character/word choices and accepts:

• a number selecting an offered option;
• s to stop;
• x to backspace;
• o to type a custom token.

Do not use interactive mode in unattended scripts, CI jobs, notebooks without stdin, or background agent runs.

File and weight behavior

• generate_to_file(destination_path, **kwargs) overwrites the destination and writes one generated text per line.
• save(weights_path="textgenrnn_weights_saved.hdf5") saves weights only.
• load(weights_path) reloads weights into the current architecture and current vocabulary/config. For custom architectures, instantiate with the correct config_path and vocab_path before loading weights.
• Training workflows may create weights, vocab, and config files; route training questions to training.

Synthesis behavior

Import synthesis helpers from textgenrnn.utils:

from textgenrnn.utils import synthesize, synthesize_to_file

Pass a list of instantiated textgenrnn objects. Synthesis shuffles model order for each generated text and switches models at stop tokens by default. Use stop_tokens=[] to switch after every character for character-level models.

Use the bundled helper script

From the generated skill directory or any other working directory with a compatible installed textgenrnn environment:

python sub-skills/generation/scripts/smoke_generate.py \
  --n 2 --prefix "Once" --temperature 0.2,1.0 --max-gen-length 80 \
  --output-file generated.txt

Use --weights-path, --vocab-path, and --config-path together for a custom scratch-trained model. Add --quiet when only the output file matters.