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Nemotron
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Table of Contents

Nemotron

  • Home
  • Application Examples
  • Deployment Guides

Nemotron Step Basics

  • About
  • Basics
  • Getting Started
  • Airgap Environment

Data Curation

  • About
  • Getting Started
  • Tasks
    • Run Curation on Local JSONL
    • Use a Hugging Face Snapshot
    • Enable Curation Filters
  • Reference
    • curate/nemo_curator CLI
    • curate/nemo_curator Configuration
    • Curation Input and Output Format
    • Curation Troubleshooting

Synthetic Data Generation

  • About
  • Getting Started
  • Tips for Using Agents
  • Planning
  • Tasks
    • Create a Domain Dataset
    • Create Tool-Calling Dataset
    • Generate Preference Data for DPO
    • Dispatch SDG to a Cluster
    • Tips for the Data Generation Pipeline
  • Reference
    • Config Schema
    • CLI Reference
    • Output Projections
    • Troubleshooting

Translation

  • About
  • Getting Started
  • Tips for Using Agents
  • Concepts
    • Pipeline Overview
    • Segmentation
    • FAITH Evaluation Inside Translation
  • Tasks
    • Run LLM Translation
    • Run NMT Translation
    • Run Google or AWS Translation
    • Configure Fields and Output
    • Use Fine Segmentation
    • Run FAITH Evaluation
  • Reference
    • Translation YAML Reference
    • CLI Reference for Translation
    • Input and Output Format
    • Troubleshooting

Build MCQ Benchmarks

  • About
  • Getting Started
  • Concepts
    • Pipeline Overview
    • Data Preparation
    • Get the Right Questions
    • Question Generation
    • Quality Validation
    • Easiness and Hallucination Filtering
    • Translation
  • Tasks
    • Prepare Data
    • Use Your Domain Data
    • Model Endpoints
    • Tune Prompts
    • Skip Stages
  • Reference
    • Supported Hugging Face Benchmarks
    • Output Files
    • Generation Configuration Reference
    • Translation Configuration Reference
    • Troubleshooting

Model Training

  • About
  • Getting Started
  • Tips for Using Agents
  • Concepts
    • Training Basics
    • Artifact Graph
    • Training Libraries
  • Tasks
    • Run SFT with AutoModel on Custom Data
    • Choose an SFT Backend
    • Choose a PEFT Backend
    • Choose an RL Alignment Step
    • Run Post-Training Optimization
    • Environment Profiles and Executors
    • Data and Checkpoint Formats
    • Convert Checkpoints
  • Reference
    • Nemotron Steps CLI Reference
    • Env Profile Generator
    • Step Catalog
    • Configuration Conventions
    • SFT Steps
      • sft/automodel
      • sft/megatron_bridge
    • PEFT Steps
      • peft/automodel
      • peft/megatron_bridge
    • RL Steps
      • rl/nemo_rl/dpo
      • rl/nemo_rl/rlvr
      • rl/nemo_rl/rlhf
    • Optimization Steps
      • optimize/modelopt/quantize
      • optimize/modelopt/prune
      • optimize/modelopt/distill
    • Checkpoint Conversion Steps
      • convert/hf_to_megatron
      • convert/megatron_to_hf
      • convert/merge_lora

Model Evaluation

  • About
  • Getting Started
  • Tips for Using Agents
  • Concepts
    • Pipeline Overview
    • Endpoint Types And Task Families
    • Tokenizer Alignment
  • Tasks
    • Discover The Model Evaluation Step
    • Run A Hosted Evaluation
    • Evaluate A Deployed Checkpoint
  • Reference
    • Configuration Reference
    • CLI Reference
    • Output Artifacts
    • Tasks Catalog
    • Troubleshooting

Training Recipes

  • Nemotron 3 Nano
    • Stage 0: Pretraining
    • Stage 1: Supervised Fine-Tuning (SFT)
    • Stage 2: Reinforcement Learning (RL)
    • Stage 3: Evaluation
    • Importing Models and Data
  • Nemotron 3 Omni
    • Stage 0: Supervised Fine-Tuning (SFT)
    • Stage 1: Reinforcement Learning (RL)
    • RL Data Preparation
    • Nemotron 3 Nano Omni — Architecture
    • Nemotron 3 Nano Omni — Inference & Deployment
  • Nemotron 3 Super
    • Stage 0: Pretraining
    • Stage 1: Supervised Fine-Tuning (SFT)
    • Stage 2: Reinforcement Learning (RL)
      • Multi-Environment RLVR (Stages 1.1–1.3)
      • SWE-RL (Stages 2.1–2.2)
      • RLHF (Stage 3)
      • RL Data Preparation
    • Multi-Environment RLVR (Stages 1.1–1.3)
    • SWE-RL (Stages 2.1–2.2)
    • RLHF (Stage 3)
    • RL Data Preparation
    • Stage 4: Evaluation
    • Stage 3: Quantization
  • Llama Nemotron Embed
  • Artifact Types & Lineage

Nano3 Stages

  • Stage 0: Pretraining
  • Stage 1: Supervised Fine-Tuning (SFT)
  • Stage 2: Reinforcement Learning (RL)
  • Stage 3: Evaluation
  • Importing Models and Data

Omni3 Stages

  • Nemotron 3 Omni Training Recipe
    • Stage 0: Supervised Fine-Tuning (SFT)
    • Stage 1: Reinforcement Learning (RL)
    • RL Data Preparation
    • Nemotron 3 Nano Omni — Architecture
    • Nemotron 3 Nano Omni — Inference & Deployment
  • Stage 0: Supervised Fine-Tuning (SFT)
  • Stage 1: Reinforcement Learning (RL)
  • RL Data Preparation
  • Nemotron 3 Nano Omni — Architecture
  • Nemotron 3 Nano Omni — Inference & Deployment

Super3 Stages

  • Nemotron 3 Super Training Recipe
    • Stage 0: Pretraining
    • Stage 1: Supervised Fine-Tuning (SFT)
    • Stage 2: Reinforcement Learning (RL)
      • Multi-Environment RLVR (Stages 1.1–1.3)
      • SWE-RL (Stages 2.1–2.2)
      • RLHF (Stage 3)
      • RL Data Preparation
    • Multi-Environment RLVR (Stages 1.1–1.3)
    • SWE-RL (Stages 2.1–2.2)
    • RLHF (Stage 3)
    • RL Data Preparation
    • Stage 4: Evaluation
    • Stage 3: Quantization
  • Stage 0: Pretraining
  • Stage 1: Supervised Fine-Tuning (SFT)
  • Stage 2: Reinforcement Learning (RL)
    • Multi-Environment RLVR (Stages 1.1–1.3)
    • SWE-RL (Stages 2.1–2.2)
    • RLHF (Stage 3)
    • RL Data Preparation
  • Multi-Environment RLVR (Stages 1.1–1.3)
  • SWE-RL (Stages 2.1–2.2)
  • RLHF (Stage 3)
  • RL Data Preparation
  • Stage 4: Evaluation
  • Stage 3: Quantization

Nemotron Kit

  • Nemotron Kit
  • NVIDIA AI Stack
  • nemo_runspec Package
  • Execution through NeMo-Run
  • OmegaConf Configuration System
  • Artifact Tracking
  • Weights & Biases Integration
  • CLI Framework
  • Data Preparation Module
  • Xenna Pipeline Observability

Data Recipes

  • Nemotron-CC Data Curation
  • Long-Document SDG

Architecture

  • Nemotron Architecture
  • Design Philosophy
  • CLI Architecture
  • Runspec: [tool.runspec] Specification
  • Curation How-To Guides

Curation How-To Guides#

Use these guides after the local initial validation in Getting Started With Data Curation.

  • Run Curation on Local JSONL
  • Use a Hugging Face Snapshot
  • Enable Curation Filters

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Getting Started With Data Curation

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Run Curation on Local JSONL
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