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NeMo-AutoModel

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NeMo-AutoModel - Home NeMo-AutoModel - Home

NeMo-AutoModel
  • GitHub

Table of Contents

Get Started

  • Introduction to the NeMo Automodel Repository
  • Install NeMo Automodel
  • YAML Configuration and Environment Variable Interpolation
  • 🤗 Hugging Face Transformers API Compatibility
  • Run on Your Local Workstation
  • Run on a Cluster (Slurm / Multi-node)

Announcements

  • Accelerating Large-Scale Mixture-of-Experts Training in PyTorch with NeMo Automodel
  • Challenges in Enabling PyTorch Native Pipeline Parallelism for Hugging Face Transformer Models
  • Google Gemma 3n: Efficient Multimodal Fine-tuning Made Simple
  • Fine-tune Hugging Face Models Instantly with Day-0 Support with NVIDIA NeMo AutoModel

Performance

  • NeMo Automodel Performance Summary

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
  • Pretraining using 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
  • Quantization-Aware Training (QAT) in NeMo Automodel
  • Model Training on Databricks

Model Coverage

  • Overview
  • Large Language Models (LLMs)
  • Vision Language Models (VLMs)

Datasets

  • Dataset Overview: LLM, VLM, and Retrieval Datasets in NeMo Automodel
  • Integrate Your Own Text Dataset
  • Biencoder Retrieval Dataset (Embedding Fine-tuning)
  • Use the ColumnMappedTextInstructionDataset
  • Use the ColumnMappedTextInstructionIterableDataset (Streaming)
  • Integrate Your Own Multi-Modal Dataset

Development

  • Checkpointing in NeMo Automodel
  • 🚀 Gradient (Activation) Checkpointing in NeMo-AutoModel
  • Pipeline Parallelism with AutoPipeline
  • Knowledge Distillation with NeMo-AutoModel
  • FP8 Training in NeMo Automodel
  • MLflow Logging in NeMo Automodel
  • API Reference
    • nemo_automodel
      • nemo_automodel.components
        • nemo_automodel.components.loss
        • nemo_automodel.components.training
        • nemo_automodel.components.datasets
        • nemo_automodel.components._peft
        • nemo_automodel.components.config
        • nemo_automodel.components.distributed
        • nemo_automodel.components.utils
        • nemo_automodel.components.moe
        • nemo_automodel.components.loggers
        • nemo_automodel.components.optim
        • nemo_automodel.components.checkpoint
        • nemo_automodel.components.launcher
        • nemo_automodel.components.attention
        • nemo_automodel.components.quantization
        • nemo_automodel.components.models
      • nemo_automodel._diffusers
        • nemo_automodel._diffusers.auto_diffusion_pipeline
      • nemo_automodel.autonvtx
      • nemo_automodel._transformers
        • nemo_automodel._transformers.auto_model
        • nemo_automodel._transformers.utils
        • nemo_automodel._transformers.model_init
        • nemo_automodel._transformers.auto_tokenizer
        • nemo_automodel._transformers.infrastructure
        • nemo_automodel._transformers.registry
        • nemo_automodel._transformers.kernel_patches
      • nemo_automodel.shared
        • nemo_automodel.shared.utils
        • nemo_automodel.shared.import_utils
        • nemo_automodel.shared.torch_patches
  • API Reference
  • nemo_automodel
  • nemo_automodel.components
  • nemo_automodel.components.optim

nemo_automodel.components.optim#

Submodules#

  • nemo_automodel.components.optim.scheduler
  • nemo_automodel.components.optim.utils

Package Contents#

Data#

__all__

API#

nemo_automodel.components.optim.__all__#

[‘OptimizerParamScheduler’, ‘build_dion_optimizer’, ‘is_dion_optimizer’]

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Last updated on 2026-02-13.