NVIDIA-NeMo/Skills

Health v2 · maintenance only

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1031 stars · 198 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Nemo-Skills is a collection of Python pipelines for improving the skills of large language models, covering synthetic data generation, model training, and evaluation on a wide range of benchmarks. It scales seamlessly from a local workstation to large Slurm clusters with a one-line configuration change.

Use cases

• generate synthetic training data for LLMs at scale
• evaluate LLMs on math, code, and science benchmarks
• run LLM inference on a Slurm cluster with vLLM or TensorRT-LLM
• fine-tune and train large language models
• benchmark models on AIME, SWE-bench, GPQA and other evals
• scale data generation jobs from one GPU to thousands of GPUs

When to choose

• you need end-to-end LLM development pipelines from data generation to training to evaluation
• you run workloads on Slurm HPC clusters and want scalable LLM jobs
• you want to evaluate models across many diverse benchmarks with parallelization
• you need to switch between API providers and self-hosted inference servers easily

When to avoid

• you only need simple single-GPU inference without cluster orchestration
• you want lightweight benchmark harnesses outside the NVIDIA ecosystem
• you need benchmark rollouts specifically, since those are moving to NeMo-Gym

Facets

framework · maturity active

llm-inference llm-training machine-learning benchmarking data-generation etl large-language-models machine-learning artificial-intelligence gpu-computing developer-tools python cli slurm synthetic-data-generation model-evaluation nemo vllm tensorrt-llm pipelines gpu linux docker

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markdown · JSON · MCP: product_card(name="NVIDIA-NeMo/Skills")