Browse Skills

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.

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.

Train large language models (7B-70B+) using RLHF techniques like PPO, GRPO, RLOO, and DPO with distributed Ray and vLLM acceleration, achieving 2× faster training than DeepSpeedChat.

Fine-tune large language models (7B-70B) using parameter-efficient methods like LoRA and QLoRA. Ideal when memory is limited, training only a fraction of parameters with minimal quality loss.

Prompt Guard detects prompt injection and jailbreak attempts in LLM apps. It can also screen third-party text such as API responses or RAG documents before they reach a model.

PyTorch FSDP2 adds fully_shard-based distributed sharding to training scripts with the right init, mixed precision, offload, optimizer, and checkpointing flow. Use it when a model does not fit on one GPU or you need DTensor-based sharding with DeviceMesh.

PyTorch Lightning organizes PyTorch training into LightningModule and Trainer abstractions with built-in logging, checkpoints, and distributed training. Use it when you want cleaner training loops and the same code to run from a laptop to a multi-GPU system.

Provides guidance for performing causal interventions on PyTorch models using pyvene. Use for causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.