NeMo-Skills

NeMo-Skills is a collection of pipelines to improve "skills" of large language models (LLMs). We support everything needed for LLM development, from synthetic data generation, to model training, to evaluation on a wide range of benchmarks. Start developing on a local workstation and move to a large-scale Slurm cluster with just a one-line change.

Here are some of the features we support:

  • Flexible LLM inference:
    • Seamlessly switch between API providers, local server and large-scale Slurm jobs for LLM inference.
    • Host models (on 1 or many nodes) with TensorRT-LLM, vLLM, sglang or Megatron.
    • Scale SDG jobs from 1 GPU on a local machine all the way to tens of thousands of GPUs on a Slurm cluster.
  • Model evaluation:
    • Evaluate your models on many popular benchmarks.
      • Math problem solving: hmmt_feb25, brumo25, aime24, aime25, omni-math (and many more)
      • Formal proofs in Lean: minif2f, proofnet
      • Coding skills: scicode, livecodebench, human-eval, mbpp
      • Chat/instruction following: ifbench, ifeval, arena-hard
      • General knowledge: mmlu, mmlu-pro, gpqa
      • Long context: ruler
    • Easily parallelize each evaluation across many Slurm jobs, self-host LLM judges, bring your own prompts or change benchmark configuration in any other way.
  • Model training: Train models using NeMo-Aligner, NeMo-RL or verl.

To get started, follow these steps, browse available pipelines or run ns --help to see all available commands and their options.

You can find more examples of how to use NeMo-Skills in the tutorials page.

We've built and released many popular models and datasets using NeMo-Skills. See all of them in the Papers & Releases documentation.