---

name: "generation" description: "Routes GPT2-Chinese prompt-based text generation and batch sample export." disable-model-invocation: true metadata: disco-role: operating license: MIT

Generation

Use this sub-skill when the task is about generating text from a checkpoint with generate.py or generate_texts.py.

Read first

• ../references/workflows.md for the end-to-end generation flow.
• ../references/cli-reference.md for the exact flags and output paths.
• ../references/model-overview.md when you need to match a checkpoint to a vocabulary bundle.
• ../references/troubleshooting.md and this sub-skill's troubleshooting file for loop, prompt, and sample-saving issues.
• ../../scripts/check_install.py when you want to confirm the environment can instantiate the model and sample a token sequence.

What belongs here

• Prompted generation from a checkpoint directory.
• generate.py for interactive or single-prompt generation.
• generate_texts.py for batch generation by title list.
• Sampling controls such as --prefix, --length, --temperature, --topk, --topp, and --repetition_penalty.
• Sample saving via --save_samples or --save_path.

What does not belong here

• Corpus preprocessing and checkpoint creation belong in training.
• Tokenizer or vocabulary creation belongs in tokenization.
• Generic sampling theory without this repo's CLI behavior belongs elsewhere.

How to route a generation request

1. Pick the output shape.
   • One prompt, one stream of text: use generate.py.
   • Multiple titles or articles on disk: use generate_texts.py.
2. Choose the checkpoint and vocab pair.
   • model_path or model_path should point at a saved checkpoint directory.
   • tokenizer_path must match the config and checkpoint vocab size.
3. Choose the prompt form.
   • The usual checkpoints expect a [CLS]-prefixed prompt.
   • The prompt is tokenized before sampling, so keep the tokenizer mode in mind.
4. Choose the decoding behavior.
   • --fast_pattern is the preferred smoke path.
   • --topk, --topp, and --temperature shape the sampling distribution.
5. Choose the save target.
   • --save_samples writes a samples.txt file.
   • generate_texts.py writes one text file per generated article.

Common decision points

• If the user wants a quick demo, keep --batch_size 1 and use a short length.
• If the user wants file-based batch output, use the title list interface instead of copy-pasting one prompt at a time.
• If the user is asking why the same sample repeats, check nsamples versus batch_size first.
• If the task is really about tokenization quality or vocabulary choice, route back to the tokenization sub-skill.

Output expectations

• Printed samples on stdout for the interactive path.
• samples.txt when --save_samples is used.
• <title-index>-<article-index>.txt files when generate_texts.py is used.