NVIDIA NeMo Skill

Comprehensive assistance with NVIDIA NeMo development, the enterprise AI platform for building, customizing, and deploying generative AI agents at scale.

When to Use This Skill

This skill should be triggered when:

Core NeMo Components:

• Working with NeMo Retriever for RAG pipelines and document extraction
• Using NeMo Customizer for fine-tuning LLMs (LoRA, SFT, DPO, GRPO)
• Implementing NeMo Guardrails for content safety and jailbreak prevention
• Building with NeMo Curator for data processing and synthetic data generation
• Using NeMo Evaluator for benchmarking LLMs, RAG, and agents
• Working with NeMo Agent Toolkit for multi-agent orchestration

NVIDIA Nemotron Models:

• Deploying Nemotron Nano, Super, or Ultra models for agentic AI
• Using Nemotron RAG models for embedding/reranking
• Implementing Nemotron Safety Guard for content moderation

Use Cases:

• Building RAG pipelines with enterprise document retrieval
• Fine-tuning models for domain-specific tasks
• Creating AI agents with tool calling and function execution
• Processing and curating training data at scale
• Implementing multi-modal AI (text, vision, audio, video)
• Deploying guardrails for safe, compliant AI applications

Quick Reference

RAG with NeMo Retriever

Basic Embedding Generation

import requests

# NeMo Retriever Embedding NIM
url = "https://integrate.api.nvidia.com/v1/embeddings"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {api_key}"
}

payload = {
    "input": ["What is retrieval-augmented generation?"],
    "model": "nvidia/nv-embedqa-e5-v5",
    "input_type": "query"
}

response = requests.post(url, json=payload, headers=headers)
embeddings = response.json()["data"][0]["embedding"]

Reranking Results

# NeMo Retriever Reranking NIM
url = "https://integrate.api.nvidia.com/v1/ranking"

payload = {
    "model": "nvidia/nv-rerankqa-mistral-4b-v3",
    "query": {"text": "What is machine learning?"},
    "passages": [
        {"text": "Machine learning is a subset of AI..."},
        {"text": "Python is a programming language..."},
        {"text": "ML models learn patterns from data..."}
    ]
}

response = requests.post(url, json=payload, headers=headers)
ranked_results = response.json()["rankings"]

Model Customization with NeMo Customizer

Submit Fine-Tuning Job

# Fine-tune with LoRA
payload = {
    "name": "custom-model-lora",
    "model": "meta/llama-3.1-8b-instruct",
    "method": "lora",
    "dataset": "s3://my-bucket/training-data.jsonl",
    "hyperparameters": {
        "learning_rate": 1e-4,
        "batch_size": 8,
        "epochs": 3,
        "lora_rank": 8
    }
}

response = requests.post(
    "http://nemo-customizer:8000/v1/customization/jobs",
    json=payload
)
job_id = response.json()["id"]

Check Job Status

# Monitor customization progress
status_response = requests.get(
    f"http://nemo-customizer:8000/v1/customization/jobs/{job_id}"
)

print(f"Status: {status_response.json()['status']}")
print(f"Progress: {status_response.json()['progress']}%")

Guardrails with NeMo Guardrails

Initialize Guardrails

from nemoguardrails import RailsConfig, LLMRails

# Load configuration
config = RailsConfig.from_path("./config")
rails = LLMRails(config)

# Apply guardrails
response = rails.generate(
    messages=[{"role": "user", "content": "Tell me about..."}]
)

YAML Configuration for Topic Control

# config.yml
models:
  - type: main
    engine: nvidia_ai_endpoints
    model: meta/llama-3.1-70b-instruct

rails:
  input:
    flows:
      - check jailbreak
      - check topic relevance
  output:
    flows:
      - check hallucination
      - check safety

Custom Rail Definition

# Custom topic control
define user ask about competitors
  "Tell me about competing products"
  "What do you think of [competitor]"

define bot refuse competitors
  "I can only discuss our own products and services."

define flow
  user ask about competitors
  bot refuse competitors
  stop

Data Curation with NeMo Curator

Text Processing Pipeline

from nemo_curator import ScoreFilter, DedupFilter
from nemo_curator.datasets import DocumentDataset

# Load dataset
dataset = DocumentDataset.read_json("data.jsonl")

# Quality filtering
quality_filter = ScoreFilter(
    score_field="quality_score",
    score_threshold=0.7
)
dataset = quality_filter(dataset)

# Deduplication
dedup_filter = DedupFilter()
dataset = dedup_filter(dataset)

# Save processed data
dataset.to_json("processed_data.jsonl")

Synthetic Data Generation

from nemo_curator.synthetic import PromptTemplate, generate_data

# Define prompt template
template = PromptTemplate(
    system="You are a helpful assistant.",
    user_template="Generate a question about {topic}"
)

# Generate synthetic data
synthetic_data = generate_data(
    template=template,
    topics=["machine learning", "data science"],
    model="nvidia/llama-3.1-nemotron-70b-instruct",
    num_samples=100
)

Evaluation with NeMo Evaluator

Academic Benchmark Evaluation

from nemo_evaluator import Evaluator

evaluator = Evaluator()

# Run MMLU benchmark
results = evaluator.evaluate(
    model="meta/llama-3.1-8b-instruct",
    tasks=["mmlu"],
    batch_size=8
)

print(f"MMLU Score: {results['mmlu']['acc']}")

RAG Pipeline Evaluation

# Evaluate RAG with custom metrics
rag_results = evaluator.evaluate_rag(
    model="custom-rag-pipeline",
    metrics=["faithfulness", "answer_relevance", "context_precision"],
    dataset="custom_qa_dataset.jsonl"
)

Agent Development with NeMo Agent Toolkit

Define Agent with Tools

# agent_config.yaml
agents:
  - name: customer_support_agent
    model: nvidia/llama-3.1-nemotron-70b-instruct
    tools:
      - web_search
      - knowledge_base_query
      - ticket_creation
    max_iterations: 5

Tool Registration

from nemo_agent_toolkit import Agent, Tool

# Define custom tool
@Tool(
    name="database_query",
    description="Query customer database for information"
)
def query_database(customer_id: str) -> dict:
    # Tool implementation
    return {"name": "John Doe", "status": "Premium"}

# Create agent
agent = Agent.from_config("agent_config.yaml")
agent.register_tool(query_database)

# Run agent
response = agent.run("What is the status of customer ID 12345?")

Deployment with NeMo NIMs

Deploy Custom Model as NIM

# Pull NIM container
docker pull nvcr.io/nim/meta/llama-3.1-8b-instruct:latest

# Run NIM with custom LoRA
docker run -d \
  --gpus all \
  -p 8000:8000 \
  -e NGC_API_KEY=$NGC_API_KEY \
  -e PEFT_MODEL_PATH=/models/custom-lora \
  -v ./models:/models \
  nvcr.io/nim/meta/llama-3.1-8b-instruct:latest

Query NIM Endpoint

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="not-used"
)

response = client.chat.completions.create(
    model="meta/llama-3.1-8b-instruct",
    messages=[
        {"role": "user", "content": "Explain quantum computing"}
    ],
    temperature=0.7,
    max_tokens=500
)

Key Concepts

NeMo Ecosystem Architecture

NeMo Suite Components:

• NeMo Retriever: RAG components (embedding, reranking, extraction)
• NeMo Customizer: Model fine-tuning and alignment (LoRA, SFT, DPO, GRPO)
• NeMo Guardrails: Safety orchestration and content moderation
• NeMo Curator: Data processing and synthetic data generation
• NeMo Evaluator: Benchmarking and evaluation pipelines
• NeMo Agent Toolkit: Multi-agent orchestration and optimization

NVIDIA Nemotron Models:

• Nano (8B): Edge deployment, fast inference, cost-efficient
• Super (70B): Single GPU, balanced accuracy/compute
• Ultra (405B): Data center scale, highest accuracy
• Nano VL: Vision-language for document intelligence
• RAG Models: Embedding and reranking for retrieval

RAG Pipeline Components

1. Document Extraction (NeMo Retriever)

• Multi-modal extraction: text, charts, tables, graphs
• 15x faster PDF processing than traditional methods
• Maintains document structure and relationships

2. Embedding (Nemotron Embedding Models)

• State-of-the-art accuracy on ViDoRe, MTEB benchmarks
• Multi-lingual support
• Optimized for enterprise documents

3. Vector Storage (cuVS)

• GPU-accelerated indexing and search
• 35x better storage efficiency
• Scalable to billions of embeddings

4. Reranking (Nemotron Reranking Models)

• Final relevance scoring
• 50% better accuracy over baseline
• Context-aware ranking

Customization Methods

LoRA (Low-Rank Adaptation)

• Parameter-efficient fine-tuning
• Fast training, low memory
• Ideal for multiple task adaptations

SFT (Supervised Fine-Tuning)

• Full model fine-tuning
• Task-specific optimization
• Higher resource requirements

DPO (Direct Preference Optimization)

• Alignment without reward model
• Human feedback integration
• Simpler than RLHF

GRPO (Group Relative Policy Optimization)

• Advanced RL alignment
• Multi-objective optimization
• Enterprise-grade policy learning

Guardrails Architecture

Rail Types:

• Input Rails: Pre-LLM validation (jailbreak, topic control)
• Output Rails: Post-LLM checks (safety, hallucination)
• Dialog Rails: Conversation flow management
• Retrieval Rails: RAG grounding verification

Orchestration:

• Parallel rail execution for low latency
• GPU acceleration for speed
• Enterprise-grade scaling

Reference Files

api.md

NeMo Customizer API Documentation

• REST API endpoints for model customization
• Job submission and monitoring
• LoRA, SFT, DPO, GRPO configuration
• Hyperparameter tuning guides
• Integration with NIM deployment

retriever.md

NeMo Retriever Models & Pipeline

• Embedding model APIs (NV-Embed-v2)
• Reranking model usage (NV-RerankQA)
• Document extraction workflows
• Vector database integration (cuVS)
• RAG pipeline architecture
• Performance benchmarks (ViDoRe, MTEB)

rag.md

RAG Implementation & Best Practices

• End-to-end RAG pipeline examples
• NeMo Evaluator for RAG metrics
• Knowledge base integration
• Multi-modal retrieval strategies
• AI-Q Blueprint for enterprise RAG

other.md

Comprehensive NeMo Ecosystem

• NeMo Curator: Data processing, synthetic generation
• NeMo Guardrails: Safety orchestration
• NeMo Agent Toolkit: Multi-agent systems
• Nemotron Models: Model family overview
• NeMo Evaluator: Benchmarking workflows
• Integration patterns across components

Working with This Skill

For Beginners

Start Here:

1. Review the Key Concepts section to understand the NeMo ecosystem
2. Explore Quick Reference for hands-on examples
3. Read retriever.md for RAG fundamentals
4. Try the basic embedding and reranking examples

First Project Ideas:

• Build a simple RAG chatbot with NeMo Retriever
• Fine-tune a small model with NeMo Customizer
• Add basic guardrails to an existing LLM app

For Intermediate Users

Focus Areas:

1. RAG Optimization: Study rag.md for advanced retrieval patterns
2. Model Customization: Use api.md to fine-tune with LoRA/SFT
3. Multi-Agent Systems: Explore NeMo Agent Toolkit in other.md
4. Evaluation: Implement benchmarking with NeMo Evaluator

Common Workflows:

• Build production RAG with reranking
• Create domain-specific models via fine-tuning
• Implement comprehensive guardrails
• Orchestrate multi-agent workflows

For Advanced Users

Enterprise Patterns:

1. Data Flywheels: Curator → Customizer → Evaluator → Production
2. Multi-Modal RAG: Vision + text retrieval with Nemotron Nano VL
3. RL Alignment: Advanced GRPO/DPO for policy optimization
4. Agent Orchestration: Complex multi-agent systems with MCP

Performance Optimization:

• GPU acceleration with cuVS for vector search
• Parallel rail execution in Guardrails
• Batch processing in Curator
• Distributed evaluation in NeMo Evaluator

Scaling Strategies:

• Kubernetes deployment of NIM microservices
• Multi-GPU customization jobs
• Enterprise data processing pipelines
• Production monitoring and observability

Navigation Tips

Quick Lookups:

• API endpoints → api.md
• RAG metrics → rag.md
• Model specs → other.md (Nemotron section)
• Safety rails → other.md (Guardrails section)

Deep Dives:

• Complete RAG pipeline → retriever.md + rag.md
• Fine-tuning workflow → api.md + other.md (Customizer)
• Agent development → other.md (Agent Toolkit)
• Data processing → other.md (Curator)

Integration Patterns

NeMo + NIM Deployment

Data Curation (Curator)
  → Model Training/Fine-tuning (Customizer)
  → Evaluation (Evaluator)
  → Deployment (NIM)
  → Monitoring (Agent Toolkit)
  → Safety (Guardrails)

Enterprise RAG Stack

Documents
  → Extraction (NeMo Retriever)
  → Vector DB (cuVS)
  → Embedding (Nemotron RAG)
  → Reranking (Nemotron RAG)
  → LLM (Nemotron + NIM)
  → Guardrails (NeMo Guardrails)
  → Response

Data Flywheel

User Interactions
  → Data Collection
  → Curation (Curator)
  → Fine-tuning (Customizer)
  → Evaluation (Evaluator)
  → Deployment (NIM)
  → Loop Back

Model Selection Guide

Use Case	Recommended Model	Deployment
Edge AI, IoT	Nemotron Nano 8B	Single device
Chatbots, agents	Nemotron Super 70B	Single GPU
Enterprise RAG	Nemotron Ultra 405B	Data center
Document intelligence	Nemotron Nano VL	GPU workstation
Embedding	NV-Embed-v2	NIM microservice
Reranking	NV-RerankQA	NIM microservice

Performance Benchmarks

NeMo Retriever:

• #1 on ViDoRe V1, V2 leaderboards (visual document retrieval)
• #1 on MTEB VisualDocumentRetrieval
• 15x faster PDF extraction vs. traditional methods
• 35x better storage efficiency with cuVS

Nemotron Models:

• Up to 6x faster throughput vs. leading 8B models
• 60% lower token generation with thinking budget
• State-of-the-art accuracy on agentic benchmarks

Resources

Official Links

• NeMo Developer Portal
• NeMo GitHub
• NGC Catalog
• API Documentation

Learning Resources

• NeMo Tutorials and Webinars (see reference docs)
• AI-Q Blueprint for RAG patterns
• NVIDIA DLI Courses on Generative AI
• Technical blogs and case studies

Community

• NVIDIA Developer Forums
• Discord channels for NeMo users
• GitHub issues for bug reports
• Feature voting for roadmap input

Notes

• NeMo is the enterprise AI platform for the full agent lifecycle
• All components are API-first and cloud-native
• Models support OpenAI-compatible APIs for easy integration
• Nemotron models are open with training data and recipes
• NIM microservices enable deployment on any GPU-accelerated system
• Supports MCP (Model Context Protocol) for tool integration

Updating

To refresh this skill with updated documentation:

1. Re-run the scraper with the same configuration
2. The skill will be rebuilt with the latest information
3. Check for new models, features, and API changes regularly