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name: textgenrnn description: "Routes agents through textgenrnn generation, training, and embedding-analysis workflows for pretrained samples, custom text generation, fine-tuning, context labels, and similarity checks." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

textgenrnn

Use this skill for the textgenrnn Python package itself. It is a small Keras/TensorFlow text-generation library centered on one public class, textgenrnn, plus a few helper functions in textgenrnn.utils.

If the request is about a different TensorFlow project, a transformer LLM stack, or generic Keras internals, route elsewhere. If the request is about this package, use the route map below.

Before you begin

1. Read references/repo-provenance.md when you need to check whether this skill still matches the repository checkout.
2. Use a compatible runtime. The verified stack for this repository is Python 3.11 with TensorFlow/Keras 2.15.x, numpy 1.26.4, scikit-learn, h5py, tqdm, and a setuptools version that still exposes pkg_resources.
3. Install the package with pip install textgenrnn for the published distribution, or validate a local checkout with an editable install inside a compatible environment.
4. Run a minimal import check before deeper work:

python -c "from textgenrnn import textgenrnn; print(textgenrnn.__name__)"

5. If import or model-load behavior looks suspicious, run scripts/check_textgenrnn_env.py first.
6. There is no CLI entry point in this repository. Use Python APIs or the bundled helper scripts.

Route map

generation

Use this route for pretrained generation, custom weight loading, temperature/prefix control, interactive next-token selection, writing generated text to files, saving/loading weights, and model synthesis across multiple textgenrnn instances.

training

Use this route for train_on_texts, train_new_model, train_from_file, train_from_largetext_file, context labels, word-level training, scratch-model creation, and tiny smoke-training checks.

embedding-analysis

Use this route for encode_text_vectors, PCA/t-SNE coordinates, and similarity ranking tasks.

Shared references

• references/installation-and-compatibility.md explains the verified install stack and the TensorFlow/Keras/setuptools compatibility boundary.
• references/model-overview.md summarizes the architecture, default config, model artifacts, and raw vector shape.
• references/troubleshooting.md covers cross-cutting import, model-load, output-file, and backend warnings.
• scripts/check_textgenrnn_env.py is a safe environment diagnostic and optional short generation smoke.

Quick path selection

• Ask for generation when the user wants to load a model, complete a prompt, generate samples, or save generated text.
• Ask for training when the user wants to feed new data, fine-tune, train from a file, or create a scratch model.
• Ask for embedding-analysis when the user wants vectors, similarity scores, or visualization coordinates.

Minimal package facts

• Public import: from textgenrnn import textgenrnn
• Public package version in this repository: 2.0.0
• Default pretrained assets ship as bundled textgenrnn_weights.hdf5 and textgenrnn_vocab.json files inside the package.
• CPU workflows are valid. CUDA/GPU is optional acceleration for training, not a requirement for the selected workflows.

When to read the bundled helper script

Use scripts/check_textgenrnn_env.py when the user asks whether the installed package can import, whether the environment exposes the expected TensorFlow backend, or whether a quick generation smoke works before moving on to the route-specific sub-skill.