Browse All Agent Skills

Model Merging & Fusion

Combines multiple fine-tuned AI models into a single high-performance model without requiring additional training or expensive GPU resources.

NeMo LLM Evaluator

Evaluates Large Language Models across 100+ industry-standard benchmarks using NVIDIA's enterprise-grade containerized architecture.

HQQ Model Quantization

Quantizes Large Language Models to ultra-low bit precision without requiring calibration datasets for efficient inference and fine-tuning.

SGLang Inference Serving

Optimizes LLM serving and structured data generation with RadixAttention prefix caching for high-performance agentic workflows.

NeMo Guardrails Safety Alignment

Implements programmable safety rails and runtime validation for LLM applications using NVIDIA's NeMo Guardrails framework.

HuggingFace Accelerate Distributed Training

Simplifies PyTorch distributed training across multiple GPUs, TPUs, and nodes with minimal code changes and a unified API.

SentencePiece Tokenization

Implements language-independent subword tokenization using BPE and Unigram algorithms for robust NLP model training and inference.

NVIDIA TensorRT-LLM Optimization

Optimizes Large Language Model inference for maximum throughput and ultra-low latency on NVIDIA GPUs.

OpenRLHF Model Training

Deploys and manages high-performance RLHF training pipelines for large-scale language models using Ray and vLLM acceleration.

SAELens Mechanistic Interpretability

Decomposes neural network activations into interpretable, sparse features using SAELens for deep mechanistic interpretability research.

Weights & Biases MLOps

Integrates Weights & Biases into your workflow to track machine learning experiments, visualize training metrics, and manage model artifacts in real-time.

Pyvene Causal Interventions

Facilitates causal interventions on PyTorch models using a declarative framework for mechanistic interpretability experiments.

torchforge RL Training

Implements PyTorch-native agentic reinforcement learning workflows using Meta's torchforge library for scalable algorithm experimentation.

Ray Data Scalable Processing

Facilitates high-performance distributed data processing and streaming for large-scale machine learning workloads.

Mamba Architecture Guide

Implements and optimizes Selective State Space Models (SSM) for high-performance sequence modeling and long-context AI applications.

Sentence Transformers Embeddings

Generates high-quality sentence, text, and image embeddings for RAG, semantic search, and clustering using state-of-the-art transformer models.

vLLM Inference Serving

Serves large language models with high throughput and low latency using PagedAttention and continuous batching.

LangSmith LLM Observability

Monitors, debugs, and evaluates large language model applications with comprehensive tracing and systematic testing tools.

Whisper Speech Recognition

Transcribes and translates audio across 99 languages using OpenAI's robust general-purpose speech recognition models.

Axolotl LLM Fine-Tuning

Streamlines the fine-tuning of large language models using Axolotl through expert YAML configuration and advanced training pattern guidance.

DSPy AI Programming & Optimization

Build and optimize complex AI systems using declarative programming instead of manual prompt engineering.

SkyPilot Multi-Cloud Orchestration

Automates ML workload deployment across multiple cloud providers with intelligent cost optimization and spot instance management.

LM Evaluation Harness

Evaluates Large Language Models across 60+ academic benchmarks using standardized prompts and metrics for reproducible research.

GPTQ Model Quantization

Compresses large language models to 4-bit precision to enable high-speed inference and deployment on consumer-grade hardware.

RWKV Model Architecture

Implements and manages RWKV architectures for efficient, linear-time AI inference and long-context processing.

PEFT LLM Fine-Tuning

Optimizes large language model fine-tuning using LoRA, QLoRA, and other parameter-efficient methods to significantly reduce memory and hardware requirements.

LangChain Framework Integration

Builds sophisticated LLM applications using agents, chains, and Retrieval-Augmented Generation (RAG) with a unified interface.

AWQ LLM Quantization & Optimization

Optimizes Large Language Models using 4-bit activation-aware weight quantization to achieve 3x faster inference with minimal accuracy loss.

AudioCraft Audio Generation

Generates high-quality music and sound effects from text descriptions using Meta's AudioCraft library.

BigCode Evaluation Harness

Evaluates AI code generation models using industry-standard benchmarks and pass@k metrics.