Automodel
DTensor-native pretraining and fine-tuning for LLMs/VLMs with day-0 Hugging Face support, GPU-accelerated, and memory efficient.
Stars: 66
Automodel is a Python library for automating the process of building and evaluating machine learning models. It provides a set of tools and utilities to streamline the model development workflow, from data preprocessing to model selection and evaluation. With Automodel, users can easily experiment with different algorithms, hyperparameters, and feature engineering techniques to find the best model for their dataset. The library is designed to be user-friendly and customizable, allowing users to define their own pipelines and workflows. Automodel is suitable for data scientists, machine learning engineers, and anyone looking to quickly build and test machine learning models without the need for manual intervention.
README:
NeMo Framework is NVIDIA's GPU accelerated, end-to-end training framework for large language models (LLMs), multi-modal models and speech models. It enables seamless scaling of training (both pretraining and post-training) workloads from single GPU to thousand-node clusters for both 🤗Hugging Face/PyTorch and Megatron models. It includes a suite of libraries and recipe collections to help users train models from end to end. The AutoModel library ("NeMo AutoModel") provides GPU-accelerated PyTorch training for 🤗Hugging Face models on Day-0. Users can start training and fine-tuning models instantly without conversion delays, scale effortlessly with PyTorch-native parallelisms, optimized custom kernels, and memory-efficient recipes-all while preserving the original checkpoint format for seamless use across the Hugging Face ecosystem.
⚠️ Note: NeMo AutoModel is under active development. New features, improvements, and documentation updates are released regularly. We are working toward a stable release, so expect the interface to solidify over time. Your feedback and contributions are welcome, and we encourage you to follow along as new updates roll out.
✅ Available now | 🔜 Coming in 25.09
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✅ HuggingFace Integration - Works with 1-70B models (Qwen, Llama).
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✅ Distributed Training - Fully Sharded Data Parallel (FSDP2) support.
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✅ Environment Support - Support for SLURM and interactive training.
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✅ Learning Algorithms - SFT (Supervised Fine-Tuning), and PEFT (Parameter Efficient Fine-Tuning).
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✅ Large Model Support - Native PyTorch support for models up to 70B parameters.
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✅ Advanced Parallelism - PyTorch native FSDP2, TP, CP, and SP for efficient training.
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✅ Sequence Packing - Sequence packing in both DTensor and MCore for huge training perf gains.
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✅ DCP - Distributed Checkpoint support with SafeTensors output.
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✅ HSDP - Hybrid Sharding Data Parallelism based on FSDP2.
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🔜 Pipeline Support - Torch-native support for pipelining composable with FSDP2 and DTensor (3D Parallelism).
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🔜 Pre-training - Support for model pre-training, including DeepSeekV3, GPT-OSS and Qwen3 (Coder-480B-A35B, etc).
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🔜 Knowledge Distillation - Support for knowledge distillation with LLMs; VLM support will be added post 25.09.
NeMo AutoModel provides native support for a wide range of models available on the Hugging Face Hub, enabling efficient fine-tuning for various domains. Below is a comprehensive list of all supported models with their available recipes:
To get started quickly, NeMo AutoModel provides a collection of ready-to-use recipes for common LLM and VLM fine-tuning tasks. Simply select the recipe that matches your model and training setup (e.g., single-GPU, multi-GPU, or multi-node).
| Domain | Model Family | Model ID | Recipes |
|---|---|---|---|
| LLM | LLaMA | meta-llama/Llama-3.2-1B |
SFT, PEFT |
meta-llama/Llama-3.2-3B-Instruct |
SFT, PEFT | ||
meta-llama/Llama-3.1-8B |
FP8 | ||
| LLM | Mistral | mistralai/Mistral-7B-v0.1 |
SFT, PEFT, FP8 |
mistralai/Mistral-Nemo-Base-2407 |
SFT, PEFT, FP8 | ||
mistralai/Mixtral-8x7B-Instruct-v0.1 |
PEFT | ||
| LLM | Qwen | Qwen/Qwen2.5-7B |
SFT, PEFT, FP8 |
Qwen/Qwen3-0.6B |
SFT, PEFT | ||
Qwen/QwQ-32B |
SFT, PEFT | ||
| LLM | Gemma | google/gemma-3-270m |
SFT, PEFT |
google/gemma-2-9b-it |
SFT, PEFT, FP8 | ||
google/gemma-7b |
SFT, PEFT | ||
| LLM | Phi | microsoft/phi-2 |
SFT, PEFT |
microsoft/Phi-3-mini-4k-instruct |
SFT, PEFT | ||
microsoft/phi-4 |
SFT, PEFT, FP8 | ||
| LLM | Seed | ByteDance-Seed/Seed-Coder-8B-Instruct |
SFT, PEFT, FP8 |
ByteDance-Seed/Seed-OSS-36B-Instruct |
SFT, PEFT | ||
| LLM | Baichuan | baichuan-inc/Baichuan2-7B-Chat |
SFT, PEFT, FP8 |
| VLM | Gemma | google/gemma-3-4b-it |
SFT, PEFT |
google/gemma-3n-e4b-it |
SFT, PEFT |
And more: Check out more LLM and VLM examples! Any causal LM on Hugging Face Hub can be used with the base recipe template!
To run a NeMo AutoModel recipe, you need a recipe script (e.g., LLM, VLM) and a YAML config file (e.g., LLM, VLM):
# Command invocation format:
uv run <recipe_script_path> --config <yaml_config_path>
# LLM example: multi-GPU with FSDP2
uv run torchrun --nproc-per-node=8 recipes/llm_finetune/finetune.py --config recipes/llm_finetune/llama/llama3_2_1b_hellaswag.yaml
# VLM example: single GPU fine-tuning (Gemma-3-VL) with LoRA
uv run recipes/vlm_finetune/finetune.py --config recipes/vlm_finetune/gemma3/gemma3_vl_3b_cord_v2_peft.yaml
- Day-0 Hugging Face Support: Instantly fine-tune any model from the Hugging Face Hub
- Lightning Fast Performance: Custom CUDA kernels and memory optimizations deliver 2–5× speedups
- Large-Scale Distributed Training: Built-in FSDP2 and Megatron-FSDP for seamless multi-node scaling
- Vision-Language Model Ready: Native support for VLMs (Qwen2-VL, Gemma-3-VL, etc)
- Advanced PEFT Methods: LoRA and extensible PEFT system out of the box
- Seamless HF Ecosystem: Fine-tuned models work perfectly with Transformers pipeline, VLM, etc.
- Robust Infrastructure: Distributed checkpointing with integrated logging and monitoring
- Optimized Recipes: Pre-built configurations for common models and datasets
- Flexible Configuration: YAML-based configuration system for reproducible experiments
- FP8 Precision: Native FP8 training & inference for higher throughput and lower memory use
- INT4 / INT8 Quantization: Turn-key quantization workflows for ultra-compact, low-memory training
NeMo AutoModel is offered both as a standard Python package installable via pip and as a ready-to-run NeMo Framework Docker container.
# We use `uv` for package management and environment isolation.
pip3 install uv
# If you cannot install at the system level, you can install for your user with
# pip3 install --user uv
Run every command with uv run. It auto-installs the virtual environment from the lock file and keeps it up to date, so you never need to activate a venv manually. Example: uv run recipes/llm_finetune/finetune.py. If you prefer to install NeMo Automodel explicitly, please follow the instructions below.
# Install the latest stable release from PyPI
# We first need to initialize the virtual environment using uv
uv venv
uv pip install nemo_automodel # or: uv pip install --upgrade nemo_automodel
# Install the latest NeMo Automodel from the GitHub repo (best for development).
# We first need to initialize the virtual environment using uv
uv venv
# We can now install from source
uv pip install git+https://github.com/NVIDIA-NeMo/Automodel.git
uv run python -c "import nemo_automodel; print('✅ NeMo AutoModel ready')"
distributed:
_target_: nemo_automodel.distributed.megatron_fsdp.MegatronFSDPManager
dp_size: 8
tp_size: 1
cp_size: 1
peft:
peft_fn: nemo_automodel._peft.lora.apply_lora_to_linear_modules
match_all_linear: True
dim: 8
alpha: 32
use_triton: Truemodel:
_target_: nemo_automodel._transformers.NeMoAutoModelForImageTextToText.from_pretrained
pretrained_model_name_or_path: Qwen/Qwen2.5-VL-3B-Instruct
processor:
_target_: transformers.AutoProcessor.from_pretrained
pretrained_model_name_or_path: Qwen/Qwen2.5-VL-3B-Instruct
min_pixels: 200704
max_pixels: 1003520checkpoint:
enabled: true
checkpoint_dir: ./checkpoints
save_consolidated: true # HF-compatible safetensors
model_save_format: safetensorsNeMo-Automodel/
├── nemo_automodel/ # Core library
│ ├── _peft/ # PEFT implementations (LoRA)
│ ├── _transformers/ # HF model integrations
│ ├── checkpoint/ # Distributed checkpointing
│ ├── datasets/ # Dataset loaders
│ │ ├── llm/ # LLM datasets (HellaSwag, SQuAD, etc.)
│ │ └── vlm/ # VLM datasets (CORD-v2, rdr etc.)
│ ├── distributed/ # FSDP2, Megatron FSDP, parallelization
│ ├── loss/ # Optimized loss functions
│ └── training/ # Training recipes and utilities
├── recipes/ # Ready-to-use training recipes
│ ├── llm/ # LLM fine-tuning recipes
│ └── vlm/ # VLM fine-tuning recipes
└── tests/ # Comprehensive test suite
We welcome contributions! Please see our Contributing Guide for details.
NVIDIA NeMo AutoModel is licensed under the Apache License 2.0.
- Documentation: https://docs.nvidia.com/nemo-framework/user-guide/latest/automodel/index.html
- Hugging Face Hub: https://huggingface.co/models
- Issues: https://github.com/NVIDIA-NeMo/Automodel/issues
- Discussions: https://github.com/NVIDIA-NeMo/Automodel/discussions
Made with ❤️ by NVIDIA
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