🚀 Open Colab Notebook - Try the project yourself!
- Start with quality corpus: Diverse, clean, representative data
- Choose N wisely: Balance between coherence and creativity (3-4 often works well)
- Iterate frequently: Many small updates beat few large ones
- Monitor diversity: Ensure model doesn't converge to repetitive outputs
- Save checkpoints: Keep versions to compare progress
- Open the Colab notebook
- Upload your CSV dataset (with a text column)
- Run cells sequentially:
- Build corpus
- Train n-gram model
- Generate samples
- Provide feedback
- Observe improvements
- Change N: Modify context size for different pattern complexity
- Adjust learning rate: Control how quickly model adapts to feedback
- Vary corpus: Try different text styles/domains
- Experiment with rewards: Test different feedback strategies
- Python 3.x
- pandas (for CSV handling)
- collections (defaultdict, Counter)
- random (for sampling)
- Clone this repository
- Open the Colab notebook
- Follow the step-by-step cells
- Experiment with your own data!
- N-grams capture local patterns in text
- Probabilistic generation creates varied outputs
- Reinforcement learning aligns models with human values
- EOS tokens provide natural boundaries
- Iteration is key to improvement
- Markov Chains: N-grams are Markov models (next state depends only on last N states)
- Language Modeling: Foundation for modern NLP (GPT, BERT build on these ideas)
- RLHF: This project demonstrates reinforcement learning from human feedback at a basic level
- Generative Models: Understanding generation from probability distributions
Feel free to fork, experiment, and share improvements! This is a learning project meant to demystify language models and RL.
MIT License - feel free to use for learning and experimentation.
Happy Learning! 🎉
Remember: The best way to understand these concepts is to run the code, experiment with parameters, and observe how changes affect output. Each iteration teaches you something new about how language models work.