Advanced Reasoning Data Annotation & LLM Fine-Tuning
Project Overview: Developed a high-quality dataset designed to enhance the reasoning capabilities of Large Language Models (LLMs). The project focused on "Reasoning Trace" annotation, where raw problem-solving data was transformed into a structured metacognitive format. Key Contributions: * Strategic Annotation: Formatted complex datasets into specialized chat templates using <think> tags to teach models step-by-step logical deduction. * Data Synthesis: Prepared and labeled training pairs for fine-tuning models like Nemotron and TinyLlama using LoRA (Low-Rank Adaptation) techniques. * Quality Assurance: Ensured high-fidelity data labeling to optimize model performance for reasoning-heavy tasks, specifically focusing on mathematical and logical puzzles. * Technical Implementation: Managed the end-to-end pipeline from raw data ingestion to formatted training sets, ensuring the highest level of accuracy for model convergence.