antara: markov text completions
playing with probabilities and n-grams to see how a machine might choose its next word
Aug 2025 - PresentN-gram language models, Markov chains
Markov ChainsLanguage ModelingPlayground
Live Case Study
From sequences to stories
Experimenting with n-gram Markov chains as a foundation for language modeling—building intuition for how larger LLMs extend these ideas.
Problem
How can a simple statistical model generate coherent text without deep learning?
Objectives
What success looked like
- Build an n-gram frequency table from input text
- Predict next tokens based on state probabilities
- Experiment with top-k and temperature to control diversity
Protocol
Markov completion flow
- 1Ingest corpus → tokenize into words
- 2Build n-gram frequency table
- 3Pick a seed context (user input)
- 4Sample next token (top-k + temperature)
- 5Append + shift window → repeat
- 6Render generated sequence in UI
Performance
Early runtime metrics
Median next-word lookup2ms
target 10ms
Bundle Size120KB
target 200KB
Decisions
Why these choices
Markov chains
Lightweight, interpretable foundation for text generation
Top-k + temperature
Balance between determinism and creativity in completions
Outcomes
What shipped
- Interactive text completions that are quirky yet structured
- Demonstrates why you ‘walk with n-grams before you run with LLMs’
- Provides an educational sandbox to explore probability-driven text