mwc/lab_tinylm
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chris
2026-02-09 12:26:26 -05:00
parent ce251fddbe
commit 695f9dd687
1 changed files with 6 additions and 3 deletions
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9
tlm/cli.py
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@@ -11,12 +11,14 @@ def cli():
@cli.command() @cli.command()
@click.option("--length", default=50, help="Number of words to generate.") @click.option("--length", default=50, help="Number of words to generate.")
@click.option("--n", default=2, help="Number of words in the context window.")
@click.option("--text", type=click.Path(exists=True), help="Text file to use as training corpus.") @click.option("--text", type=click.Path(exists=True), help="Text file to use as training corpus.")
@click.option("--gutenberg", help="NLTK Gutenberg corpus key (use --list-gutenberg to see available keys).") @click.option("--gutenberg", help="NLTK Gutenberg corpus key (use --list-gutenberg to see available keys).")
@click.option("--list-gutenberg", is_flag=True, help="List available Gutenberg corpus keys.") @click.option("--list-gutenberg", is_flag=True, help="List available Gutenberg corpus keys.")
@click.option("--mbox", type=click.Path(exists=True), help="Mbox file to use for training.") @click.option("--mbox", type=click.Path(exists=True), help="Mbox file to use for training.")
@click.option("--prompt", help="Prompt to start generation.")
@click.option("--interact", is_flag=True, help="Drop into interactive shell after generating.") @click.option("--interact", is_flag=True, help="Drop into interactive shell after generating.")
def generate(length, text, gutenberg, list_gutenberg, mbox, interact): def generate(length, n, text, gutenberg, list_gutenberg, mbox, prompt, interact):
"""Generate text using the language model.""" """Generate text using the language model."""
import nltk import nltk
@@ -46,9 +48,10 @@ def generate(length, text, gutenberg, list_gutenberg, mbox, interact):
raise click.UsageError("Must specify one of --text, --gutenberg, or --mbox for training data.") raise click.UsageError("Must specify one of --text, --gutenberg, or --mbox for training data.")
# Train and generate # Train and generate
model = TinyLanguageModel() model = TinyLanguageModel(n=n)
model.train(corpus) model.train(corpus)
output = model.generate(length) prompt_words = prompt.split() if prompt else None
output = model.generate(length, prompt=prompt_words)
click.echo(output) click.echo(output)
if interact: if interact: