Browse Skills

Llama Guard filters LLM inputs and outputs for unsafe content across violence, sexual content, weapons, substances, self-harm, and criminal planning. Use it to block or review prompts and responses in production chat apps.

Llm Evaluation Harness evaluates LLMs across academic benchmarks like MMLU, GSM8K, HumanEval, and TruthfulQA. Use it to benchmark models, compare results, and track training progress.

Guide for using Mamba state-space models, offering O(n) complexity and 5× faster inference for long sequences. Ideal when you need efficient processing of millions of tokens without a KV cache.

Megatron-Core Training trains large language models with NVIDIA Megatron-Core using tensor, pipeline, context, and expert parallelism. Use it for distributed pretraining of models above 1B parameters on NVIDIA GPUs.

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

ML Training Recipes gives PyTorch patterns for choosing architectures, setting optimizers and learning rates, and debugging training issues. Use it when training or fine-tuning neural networks, tuning GPU throughput, or diagnosing loss spikes and OOMs.

Modal Serverless GPU runs GPU-backed ML workloads without managing servers. Use it to deploy models as APIs, run batch jobs, or scale inference and training on demand.

A minimal, hackable GPT implementation in ~300 lines of PyTorch for learning transformer architectures from scratch. Ideal for education, prototyping, and training small language models on limited hardware.

GPU-accelerated toolkit for curating training data for LLMs, supporting text, image, video, and audio. Use for fuzzy deduplication, quality filtering, semantic deduplication, and PII redaction at scale.

NeMo Evaluator SDK evaluates LLMs across 100+ benchmarks and 18+ harnesses with containerized, reproducible runs. Use it to benchmark models on local Docker, Slurm HPC, or cloud backends.

NeMo Guardrails adds programmable runtime safety checks to LLM apps, including jailbreak detection, input/output validation, fact-checking, and PII filtering. Use it when you need configurable guardrails around model responses.

Provides guidance for interpreting and manipulating neural network internals using nnsight. Ideal for running interpretability experiments on models too large for local GPUs via remote NDIF execution, or for working with any PyTorch architecture.