Generate Sentences

Load the saved model and generate with default settings:

npx tsx src/phrases-generate.ts

loaded phrases-model.json: 63296 params, vocab 597
generating 20 sentences (temperature=0.8):

   1. the puppy wants to jump
   2. the pig swims by the park
   3. i hold the pie
   4. the fish skips
   5. the teacher is sweet
   ...

Verify: Output Makes Sense

The generated sentences should:

• Start with plausible words (“the”, “a”, “i”, “we”, “mom”, etc.)
• Follow basic subject-verb patterns
• Use words from the training vocabulary
• End naturally (not cut off mid-thought)

They will not be perfect. You will see occasional oddities like “the fish quacks” or “i toes”. The model has only 63K parameters and 5,000 training steps. But the overall structure should clearly resemble English sentences.

Try Different Sampling Strategies

Low Temperature (Conservative)

npx tsx src/phrases-generate.ts 10 --temp=0.3

With low temperature, the model almost always picks the highest-probability word. Output will be repetitive but grammatically safer.

High Temperature (Creative)

npx tsx src/phrases-generate.ts 10 --temp=1.5

With high temperature, the model considers unlikely words. Output will be more varied but may include nonsensical combinations.

Top-k Filtering

npx tsx src/phrases-generate.ts 10 --top-k=10

Only the 10 most likely words can be chosen at each position. This trims the long tail of unlikely words.

Nucleus Sampling (Top-p)

npx tsx src/phrases-generate.ts 10 --top-p=0.9

Keep the smallest set of words whose probabilities sum to 0.9. Adapts to the model’s confidence at each position.

Combined

npx tsx src/phrases-generate.ts 10 --temp=0.7 --top-p=0.9 --top-k=50

All three strategies working together. This is how production LLMs typically configure their sampling.