Train a Reasoning LLM in a Weekend

9 months ago

NVIDIA provides tools and datasets to train a small reasoning model in about 48 hours on a single GPU.
The Llama Nemotron family of open models is designed for high-performance reasoning across various tasks.
The models feature a dynamic reasoning toggle to switch between standard chat and advanced reasoning modes.
NVIDIA has open-sourced the Llama Nemotron Post-Training Dataset with over 32 million samples across domains like math, coding, and science.
Training involves data curation, fine-tuning, and evaluation, with a focus on supervised fine-tuning (SFT) for best results.
The dataset is organized into subsets for SFT or RL, with detailed metadata and sample attributes.
A recommended training approach includes using LoRA adapters for parameter-efficient fine-tuning on models with at least 8B parameters.
Evaluation shows significant improvements over base models, with performance gains of up to 10 points on benchmarks like GPQA and MMLU.
The process is scalable, with potential for further improvements by increasing training samples and time.

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