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🚀 NeMo AutoModel

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<!-- **Day-0 integration with Hugging Face models automating fine-tuning and pretraining with pytorch-native parallelism, custom-kernels and optimized recipes** **Pytorch DTensor‑native SPMD library for large‑scale training**-->

📖 Documentation • 🔥 Ready-to-Use Recipes • 💡 Examples • Model Coverage • Performance • 🤝 Contributing

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📣 News and Discussions

• [03/16/2026]Mistral Small 4 We support fine-tuning for Mistral4 119B! Check out our recipe.
• [03/11/2026]Nemotron Super v3 We support fine-tuning for nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16. Check out our recipe.
• [03/11/2026]GLM-5 We now support finetuning zai-org/GLM-5. Check out our recipe.
• [03/02/2026]Qwen3.5 small models We support finetuning for Qwen3.5 small models 0.8B, 2B, 4B (recipe) and 9B (recipe)
• [02/16/2026]Qwen3.5 MoE We support finetuning for Qwen/Qwen3.5-397B-A17B (recipe) and Qwen/Qwen3.5-35B-A3B (recipe)
• [02/13/2026]MiniMax-M2.5 We support finetuning for MiniMaxAI/MiniMax-M2.5. Checkout our recipe
• [02/11/2026]GLM-4.7-Flash We now support finetuning GLM-4.7-Flash. Checkout our packed sequence recipe
• [02/09/2026]MiniMax-M2 We support finetuning for MiniMaxAI/MiniMax-M2. Checkout our recipe
• [02/06/2026]Qwen3 VL 235B We support finetuning for Qwen/Qwen3-VL-235B-A22B-Instruct. Checkout our recipe
• [02/06/2026]GLM4.7 We now support finetuning GLM4.7. Checkout our recipe
• [02/06/2026]Step3.5-flash is out! Finetune it with our finetune recipe
• [02/05/2026]DeepSeek-V3.2 is out! Checkout out the finetune recipe!
• [02/04/2026]Kimi K2.5 VL is out! Finetune it with NeMo AutoModel
• [01/30/2026]Kimi VL We support fine-tuning for moonshotai/Kimi-VL-A3B-Instruct. Check out our recipe.
• [01/12/2026]Nemotron Flash We support fine-tuning for nvidia/Nemotron-Flash-1B. Check out our recipe.
• [01/12/2026]Nemotron Parse We support fine-tuning for nvidia/NVIDIA-Nemotron-Parse-v1.1. Check out our recipe.
• [01/07/2026]Devstral We support fine-tuning for mistralai/Devstral-Small-2512. Check out our recipe.
• [12/18/2025]FunctionGemma is out! Finetune it with NeMo AutoModel!
• [12/15/2025]NVIDIA-Nemotron-3-Nano-30B-A3B is out! Finetune it with NeMo AutoModel!
• [11/6/2025]Accelerating Large-Scale Mixture-of-Experts Training in PyTorch
• [10/6/2025]Enabling PyTorch Native Pipeline Parallelism for 🤗 Hugging Face Transformer Models
• [9/22/2025]Fine-tune Hugging Face Models Instantly with Day-0 Support with NVIDIA NeMo AutoModel
• [9/18/2025]🚀 NeMo Framework Now Supports Google Gemma 3n: Efficient Multimodal Fine-tuning Made Simple

Overview

Nemo AutoModel is a Pytorch DTensor‑native SPMD open-source training library under NVIDIA NeMo Framework, designed to streamline and scale training and finetuning for LLMs and VLMs. Designed for flexibility, reproducibility, and scale, NeMo AutoModel enables both small-scale experiments and massive multi-GPU, multi-node deployments for fast experimentation in research and production environments.

<p align="center"> <a href="https://github.com/NVIDIA-NeMo/Automodel"><picture> <source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/NVIDIA-NeMo/Automodel/refs/heads/main/docs/automodel_diagram.png"> <img alt="AutoModel Logo" src="https://raw.githubusercontent.com/NVIDIA-NeMo/Automodel/refs/heads/main/docs/automodel_diagram.png"> </picture></a> </p>

What you can expect:

• Hackable with a modular design that allows easy integration, customization, and quick research prototypes.
• Minimal ceremony: YAML-driven recipes; override any field using CLI.
• High performance and flexibility with custom kernels and DTensor support.
• Seamless integration with Hugging Face for day-0 model support, ease of use, and wide range of supported models.
• Efficient resource management using Kubernetes and Slurm, enabling scalable and flexible deployment across configurations.
• Documentation with step-by-step guides and runnable examples.

<!-- Please refer to our design documents for more details on the architecture and design philosophy. --> <!-- 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 w