Overview

This is a fully merged Qwen2.5-3B-Instruct model, fine-tuned by nnsohamnn using LoRA on 5,000 reasoning samples from the ReTrace and OpenO1-SFT datasets. The model is designed to produce structured reasoning, explicitly detailing its thought process within <Thought> tags before providing a final answer in <Output> tags. This approach enhances transparency and verifiability in problem-solving.

Key Capabilities

• Structured Reasoning: Generates explicit step-by-step thought processes and final answers.
• Multi-Domain Problem Solving: Trained on diverse reasoning examples covering math, logic, word problems, and general reasoning.
• Production Ready: Provided as a fully merged FP16 model, requiring no adapter loading, with a 6GB size.
• Efficient Training: Achieved a 49.2% reduction in training loss over 310 steps, utilizing Unsloth and HuggingFace Transformers.

Good For

• Applications requiring transparent, verifiable reasoning outputs.
• Tasks involving mathematical problem-solving and logical deduction.
• Use cases where a smaller, efficient model with strong reasoning capabilities is preferred.