NeMo-Skills provides pipelines for enhancing large language models' reasoning and problem-solving capabilities, particularly in mathematics. It targets researchers and developers working with LLMs, offering tools for data generation, model training, and evaluation, with a focus on reproducible results and state-of-the-art performance on mathematical benchmarks.

How It Works

The project leverages NeMo-Aligner for efficient model training and supports flexible inference across various backends like NeMo, vLLM, sglang, and TensorRT-LLM. It facilitates checkpoint format conversion and offers a suite of evaluation pipelines for diverse tasks including math problem-solving, formal proofs, coding, chat, and general knowledge. A key contribution is the OpenMathReasoning dataset, featuring synthetic data for mathematical reasoning.

Quick Start & Requirements

• Installation and usage details are available via a tutorial and the ns --help command.
• Requires NVIDIA NeMo framework and potentially specific hardware for training and inference.
• Links: Tutorial, Pipelines

Highlighted Details

• Achieved state-of-the-art results on mathematical benchmarks like AIME24 and HMMT-24-25 with OpenMath-Nemotron models.
• Released the OpenMathReasoning dataset (540K math problems, 3.2M CoT solutions, 1.7M TIR solutions).
• Supports seamless switching between inference servers (NeMo, vLLM, sglang, TensorRT-LLM) and checkpoint conversion.
• Includes Nemo Inspector for visualizing inference and data analysis.

Maintenance & Community

• Developed by NVIDIA, with contributions from researchers like Ivan Moshkov, Shubham Toshniwal, and Igor Gitman.
• Citations provided for related papers on arXiv.

Licensing & Compatibility

• The project is strictly for research purposes.
• No explicit license is mentioned in the README, implying potential restrictions on commercial use or closed-source integration.

Limitations & Caveats

• The project is designated for research purposes only, which may impose usage restrictions.
• Specific hardware requirements for optimal performance are not detailed but are implied by the NVIDIA ecosystem.