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LogoLogoNeMo AutoModel
    • Home
  • Get Started
    • About NeMo AutoModel
    • Key Features and Concepts
    • Install NeMo AutoModel
    • YAML Configuration
    • 🤗 Transformers API Compatibility
    • Repository Structure
  • Announcements
    • Announcements
  • NeMo AutoModel Performance
    • Performance Summary
  • Model Coverage
    • Model Coverage Overview
    • Model Release Log
  • Recipes & E2E Examples
    • Recipes and End-to-End Examples
    • Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) with NeMo AutoModel
    • Function Calling with NeMo AutoModel using FunctionGemma
    • Knowledge Distillation with NeMo AutoModel
    • Fine-Tune Large MoE LLMs
    • DeepSeek V4 Flash
    • Hy3-preview
    • Pretraining Megatron Core Datasets with NeMo AutoModel
    • LLM Pre-Training with NeMo AutoModel
    • Sequence Classification (SFT/PEFT) with NeMo AutoModel
    • Fine-Tune Gemma 3 and Gemma 3n
    • Fine-Tuning Gemma 4 31B on CORD-v2 Receipts — End-to-End Guide
    • Fine-Tune Qwen3.5-VL
    • Nemotron-Omni
    • Mistral Medium 3.5 VL
    • Diffusion Model Fine-Tuning with NeMo AutoModel
    • dLLM Fine-Tuning
    • Quantization-Aware Training (QAT) in NeMo Automodel
    • Model Training on Databricks
  • Datasets
    • Dataset Overview: LLM, VLM, and Retrieval Datasets in NeMo AutoModel
    • Integrate Your Own Text Dataset
    • Retrieval Dataset (Embedding Fine-tuning)
    • Use the ColumnMappedTextInstructionDataset
    • Use the ColumnMappedTextInstructionIterableDataset (Streaming)
    • Integrate Your Own Multi-Modal Dataset
    • Diffusion Dataset Preparation
  • Job Launchers
    • Job Launchers
    • Run on Your Local Workstation
    • Run on a Cluster
    • Run with NeMo-Run
    • Run on Any Cloud with SkyPilot
    • SkyPilot k8s
  • Development
    • Checkpointing in NeMo Automodel
    • Gradient (Activation) Checkpointing in NeMo AutoModel
    • Pipeline Parallelism with AutoPipeline
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    • Breaking Changes
      • Overview
          • Nemo Automodel
            • Autonvtx
            • Cli
            • Components
            • Package Info
            • Shared
  • Home
  • About NeMo AutoModel
  • Key Features and Concepts
  • Install NeMo AutoModel
  • YAML Configuration
  • 🤗 Transformers API Compatibility
  • Repository Structure
  • Announcements
  • Performance Summary
  • Model Coverage Overview
  • Model Release Log
  • Overview
  • Llama
  • Gemma
  • Qwen2
  • Qwen2 MoE
  • Qwen3
  • Qwen3 MoE
  • Qwen3-Next
  • DeepSeek
  • DeepSeek-V3
  • DeepSeek-V4 Flash
  • Mistral
  • Mixtral
  • Ministral3 / Devstral
  • Phi
  • Phi-3 / Phi-4
  • Phi-3-Small
  • Nemotron / Minitron
  • Nemotron-H
  • Nemotron-Flash
  • Nemotron-Super (Llama-3.3-Nemotron-Super-49B)
  • ChatGLM
  • GLM-4
  • GLM-4 MoE (GLM-4.5 / GLM-4.7)
  • GLM-5 MoE (DSA)
  • Granite
  • Granite MoE
  • Bamba
  • OLMo
  • OLMo2
  • OLMoE
  • GPT-OSS
  • GPT-2
  • GPT-J
  • GPT-NeoX / Pythia
  • StarCoder
  • StarCoder2
  • Aquila / Aquila2
  • Baichuan / Baichuan2
  • Command-R
  • Falcon
  • EXAONE
  • InternLM
  • Jais
  • MiniMax-M2
  • MiniCPM
  • Moonlight
  • Seed (ByteDance)
  • Solar Pro
  • Orion
  • StableLM
  • Step-3.5
  • GritLM
  • Hy3-preview
  • Overview
  • Kimi-VL
  • Gemma 3 VL / Gemma 3n
  • Gemma 4
  • Qwen2.5-VL
  • Qwen3-VL / Qwen3-VL-MoE
  • Qwen3.5-VL
  • Nemotron-Parse
  • Ministral3 VL
  • Mistral Medium 3.5
  • Mistral-Small-4
  • InternVL
  • Llama 4
  • LLaVA-OneVision
  • SmolVLM
  • LLaVA
  • Overview
  • Qwen3-Omni
  • Phi-4-multimodal
  • Nemotron-Omni
  • Overview
  • Wan 2.1 T2V
  • FLUX.1-dev
  • HunyuanVideo 1.5
  • Qwen-Image
  • Recipes and End-to-End Examples
  • Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) with NeMo AutoModel
  • Function Calling with NeMo AutoModel using FunctionGemma
  • Knowledge Distillation with NeMo AutoModel
  • Fine-Tune Large MoE LLMs
  • DeepSeek V4 Flash
  • Hy3-preview
  • Pretraining Megatron Core Datasets with NeMo AutoModel
  • LLM Pre-Training with NeMo AutoModel
  • Sequence Classification (SFT/PEFT) with NeMo AutoModel
  • Fine-Tune Gemma 3 and Gemma 3n
  • Fine-Tuning Gemma 4 31B on CORD-v2 Receipts — End-to-End Guide
  • Fine-Tune Qwen3.5-VL
  • Nemotron-Omni
  • Mistral Medium 3.5 VL
  • Diffusion Model Fine-Tuning with NeMo AutoModel
  • dLLM Fine-Tuning
  • Quantization-Aware Training (QAT) in NeMo Automodel
  • Model Training on Databricks
  • Dataset Overview: LLM, VLM, and Retrieval Datasets in NeMo AutoModel
  • Integrate Your Own Text Dataset
  • Retrieval Dataset (Embedding Fine-tuning)
  • Use the ColumnMappedTextInstructionDataset
  • Use the ColumnMappedTextInstructionIterableDataset (Streaming)
  • Integrate Your Own Multi-Modal Dataset
  • Diffusion Dataset Preparation
  • Job Launchers
  • Run on Your Local Workstation
  • Run on a Cluster
  • Run with NeMo-Run
  • Run on Any Cloud with SkyPilot
  • SkyPilot k8s
  • Checkpointing in NeMo Automodel
  • Gradient (Activation) Checkpointing in NeMo AutoModel
  • Pipeline Parallelism with AutoPipeline
  • FP8 Training in NeMo AutoModel
  • MLflow Logging in NeMo AutoModel
  • Breaking Changes
  • Overview
  • Nemo Automodel
  • Autonvtx
  • Cli
  • App
  • Query Capabilities
  • Utils
  • Components
  • Attention
  • Dflash Mask
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  • Interleave
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  • Fake Image
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  • Ling V2
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  • Model
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  • Llava Onevision
  • Model
  • Rice Vit
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  • Mimo V2 Flash
  • Config
  • Model
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  • Minimax M2
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  • Model
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  • Minimax M3 Vl
  • Config
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  • Vision Encoder
  • Ministral Bidirectional
  • Model
  • Mistral3
  • Model
  • Mistral3 Vlm
  • Model
  • State Dict Adapter
  • Mistral4
  • Configuration
  • Model
  • State Dict Adapter
  • Nemotron Omni
  • Model
  • State Dict Adapter
  • Nemotron Parse
  • Model
  • Nemotron Parse Loss
  • Nemotron V3
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  • Model
  • Mtp
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  • Model
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  • Model
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  • Model
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  • Qwen3 Omni Moe
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  • Step3p5
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  • Step3p7
  • Configuration Step3p7
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nemo_automodel.cli

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  • nemo_automodel.cli.app
  • nemo_automodel.cli.query_capabilities
  • nemo_automodel.cli.utils
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