This repository provides the implementation details for Multiplex Thinking, a method that uses token-wise branch-and-merge reasoning for efficient multi-pattern reasoning. It includes setup instructions using Docker or Conda, and details for training and evaluating models.

Fine-tuning a language model using LoRA (Low-Rank Adaptation) allows for efficient specialization without overwriting existing knowledge. The article details a hands-on experiment to adapt the Gemma 3 270M model for reliably masking personally identifiable information (PII) in text, showcasing the process of preparing a dataset, adding adapter layers, and training the model efficiently. Docker's ecosystem simplifies the entire fine-tuning workflow, making it accessible without requiring extensive resources.