📣 News and Discussions

• [08/29/2026]GLM-5.3 We now support full-parameter fine-tuning of Z.ai's GLM-5.3 with cuDNN DSA and HybridEP. Check out the EP64/PP4 recipe and model coverage page.
• [08/28/2026]Ox Alpha / GLM-5.3-Flash We now support fine-tuning Z.ai's 320B-A18B hybrid-attention MoE VLM with packed CP and EP. Check out the MedPix EP72/CP2 recipe and model coverage page.
• [08/26/2026]Qwen3.8-Flash-Next We now support full-parameter fine-tuning for the 180B hybrid-attention Engram MoE with FlexAttention sparse QSA, packed (THD) sequences, and model-owned context parallelism. Check out the HellaSwag EP64 recipe and model coverage page.
• [08/21/2026]DFlash 2 We now support training DFlash 2 drafters: the DFlash block-diffusion backbone plus a two-tap dynamic convolution and a pairwise path selector for higher acceptance lengths. Check out the Qwen3-8B recipe and the speculative decoding overview.
• [08/14/2026]Qwen3.8-27B We now support full-parameter SFT and LoRA fine-tuning for Qwen's dense 27B vision-language model. Check out the SFT and LoRA recipes, fine-tuning guide, and model coverage page.
• [08/12/2026]Qwen3.8-2.4T-A95B We now support full-parameter fine-tuning for Qwen/Qwen3.8-2.4T-A95B checkpoints. Check out the HellaSwag EP32/PP8 recipe and model coverage page.
• [08/12/2026]North Micro Vision We now support LoRA fine-tuning for Cohere Labs' 2.4B-parameter native-resolution vision-language model. Check out the RDR recipe and model coverage page.
• [08/10/2026]MuseGlimmer We now support SFT/LoRA the dense 30B MuseGlimmer vision-language model, including TP/CP packed sequence recipes. Check out the recipes and model coverage page.
• [08/08/2026]Nemotron 3.5 Lightning We now support LoRA fine-tuning for NVIDIA's 30B-A3B hybrid MoE model with multi-token prediction. Check out the HellaSwag recipe and model coverage page.
• [07/30/2026]Inkling-Small We now support full-parameter fine-tuning for the 276B-parameter, 12B-active Inkling-Small model on 64 H100 GPUs. Check out the MedPix EP64 recipe and model coverage page.
• [07/29/2026]Kimi K3 We now support full-parameter fine-tuning for Moonshot AI's 2.8T-parameter MoE model on NVIDIA GB200. Check out the HellaSwag EP32/PP8 recipe on 256 GB200.
• [07/21/2026]Laguna S 2.1 We now support finetuning Poolside's 118B-A8B Laguna S 2.1 MoE model. Check out our HellaSwag EP16 recipe.
• [07/18/2026]Inkling VLM MoE We now support finetuning Inkling. Check out our MedPix recipe.
• [06/27/2026] Speculative decoding in NeMo AutoModel Train target-aligned drafters end to end: EAGLE-1/2/3, P-EAGLE, DFlash, and DeepSeek's newly released DSpark. Huge thanks to @Khazic for the speculative decoding stack and @kashif for DSpark support.
• [06/21/2026]GLM-5.2 We now support finetuning zai-org/GLM-5.2 with IndexShare DSA, optional TileLang sparse kernels, and long-context CP recipes. Check out our 32K long-context recipe and model coverage page.
• [06/15/2026]DiffusionGemma. We now support fine-tuning the google/diffusiongemma-26B-A4B-it model. Check out our recipe and guide.
• [06/12/2026]MiniMax M3 We now support finetuning MiniMax's MiniMax-M3. Check out our recipe and guide.
• [06/04/2026]Nemotron-3 Ultra We now support finetuning NVIDIA's Nemotron 3 Ultra 550B A55B. Check out our recipe and guide.
• [06/03/2026]Gemma 4 12B We now support finetuning the dense google/gemma-4-12B model. Check out our recipe.
• [05/27/2026]Step-3.7-Flash We added model coverage for Stepfun AI's 198B-A13B MoE vision-language model, targeting image/video agentic developer workflows with a 256k context language backbone and 1.8B ViT vision tower. See the model coverage page.
• [05/19/2026]Ling 2.0 We now support finetuning the inclusionAI Ling 2.0 MoE family (inclusionAI/Ling-mini-2.0, inclusionAI/Ling-flash-2.0, and inclusionAI/Ling-1T), thanks to @Hayden727. Check out our recipes.
• [05/17/2026]ERNIE 4.5 and MiMo-V2-Flash We now support finetuning baidu/ERNIE-4.5-0.3B-PT, baidu/ERNIE-4.5-21B-A3B-PT, and XiaomiMiMo/MiMo-V2-Flash. Check out our ERNIE dense recipe, ERNIE MoE recipe, and MiMo recipe.
• [04/29/2026]Mistral Medium 3.5 We now support finetuning Mistral AI's 128B FP8-native VLM Mistral Medium 3.5. Check out our recipe and guide.
• [04/28/2026]Nemotron-3-Nano-Omni We now support finetuning nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16, NVIDIA's 30B-A3B omnimodal MoE (text · image · audio) with NemotronH hybrid Mamba+Attention backbone. Check out our SFT recipe, LoRA recipe, and guide.
• [04/28/2026]Hy3-preview We now support finetuning tencent/Hy3-preview, thanks to @Khazic. Check out our recipe.
• [04/25/2026]DeepSeek V4 Flash We now support finetuning deepseek-ai/DeepSeek-V4-Flash, thanks to @Khazic. Check out our recipe and guide.
• [04/22/2026]Qwen3.6-27B We now support finetuning Qwen/Qwen3.6-27B. Check out our recipe.
• [04/20/2026]Qwen-Image We now support finetuning Qwen/Qwen-Image, thanks to @harshareddy832. Check out our recipe.
• [04/16/2026]Qwen3.6 MoE We now support finetuning Qwen/Qwen3.6-35B-A3B. Check out our recipe.
• [04/16/2026]LLaVA-OneVision-1.5 We now support finetuning lmms-lab/LLaVA-OneVision-1.5-4B-Instruct, thanks to @vgauraha62. Check out our recipe.
• [04/12/2026]MiniMax-M2.7 We now support finetuning MiniMaxAI/MiniMax-M2.7. Check out our recipe.
• [04/07/2026]GLM-5.1 We now support finetuning zai-org/GLM-5.1. GLM-5.1 is Zhipu AI's latest open-source MoE model featuring MLA + DeepSeek Sparse Attention. Check out our recipe and discussion.
• [04/02/2026]Gemma 4 We support fine-tuning for Gemma4 (2B, 4B, 31B, 26BA4B)! Check out our recipes.
• [03/30/2026]NeMo AutoModel ships with agent-friendly skills in skills/ to help you with common development tasks (e.g., running a recipe, model onboarding, development) across the repo. We welcome PRs that improve existing skills or add new ones.
• [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)

Previous News

• [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 nvidia/NVIDIA-Nemotron-Parse-v1.1 (recipe, tutorial and try on Brev).
• [01/07/2026] Devstral-Small We support fine-tuning for mistralai/Devstral-Small-2-24B-Instruct-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, VLMs, diffusion models, and retrieval models. 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.

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.

Why PyTorch Distributed and SPMD

• One program, any scale: The same training script runs on 1 GPU or 1000+ by changing the mesh.
• PyTorch Distributed native: Partition model/optimizer states with DeviceMesh + placements (Shard, Replicate).
• SPMD first: Parallelism is configuration. No model rewrites when scaling up or changing strategy.
• Decoupled concerns: Model code stays pure PyTorch; parallel strategy lives in config.
• Composability: Mix tensor, sequence, and data parallel by editing placements.
• Portability: Fewer bespoke abstractions; easier to reason about failure modes and restarts.

Table of Contents

• Feature Roadmap
• Getting Started
• LLM
  • Pre-training
  • Supervised Fine-Tuning (SFT)
  • Parameter-Efficient Fine-Tuning (PEFT)
• VLM
  • Supervised Fine-Tuning (SFT)
  • Parameter-Efficient Fine-Tuning (PEFT)
• Supported Models
• Performance
• Interoperability
• Contributing
• License

TL;DR: SPMD turns “how to parallelize” into a runtime layout choice, not a code fork.

Feature Roadmap

✅ Available now (v0.5.0 / 26.06 container) | 🔜 Planned for 26.08

High-throughput scalable training

• ✅ PyTorch DTensor-native SPMD training Same training script can scale from 1 GPU to large multi-node jobs by changing the device mesh/config.
• ✅ Composable Parallelism - PyTorch native FSDP2, HSDP, TP, CP, SP and PP for distributed training.
• ✅ Optimized kernels - Uses NVIDIA-oriented kernel paths such as Transformer Engine, DeepEP, FlexAttn, TorchSDPA, fused attention, rotary embeddings, Triton, and optional kernel patches.
• ✅ MoE acceleration - Includes MoE routing and DeepEP integration, plus expert-parallel configurations used in DeepSeek, Qwen MoE, GPT-OSS, and Nemotron MoE benchmarks.
• ✅ FP8 and mixed precision - FP8 support with torchao and Transformer Engine.
• ✅ MXFP8 MoE training - Transformer Engine and torchao MXFP8 grouped-expert training on GB200.
• ✅ Activation checkpointing - Trades recomputation for lower activation memory, especially useful with FSDP and memory-efficient losses.
• ✅ Memory-efficient loss - Linear-Cut / fused linear cross entropy avoids materializing full logits for the loss, reducing output-layer memory pressure.
• ✅ Sequence packing - Packs variable-length examples together to reduce padding compute and improve GPU utilization.
• ✅ FlashAttention packed-sequence support - Packed masks can feed variable-length FlashAttention paths using per-document cu_seqlens.
• ✅ DCP - Supports PyTorch DCP and SafeTensors, sharded and consolidated layouts, merge/reshard utilities, and Hugging Face-compatible outputs.
• ✅ Async checkpointing - Can write checkpoints in the background to reduce training stalls caused by I/O.
• ✅ Dion and Muon optimizers - Distributed optimizer integrations with typed recipe configuration.
• ✅ Environment Support - SLURM, interactive, SkyPilot, and Kubernetes (via SkyPilot) launchers.

SOTA algorithms

• ✅ Pre-training - Support for model pre-training, including DeepSeekV3.
• ✅ Learning Algorithms - SFT (Supervised Fine-Tuning), PEFT (LoRA, QLoRA), and QAT (Quantization-Aware Training).
• ✅ Knowledge distillation - Support for knowledge distillation with LLMs.
• ✅ VLM knowledge distillation - Chunked KD loss and a Qwen3.5-VL teacher-student recipe.
• ✅ Speculative decoding training - Train target-aligned EAGLE-1/2/3, P-EAGLE, DFlash, DFlash 2, and DSpark drafters for faster verified generation.

Model Coverage and 🤗 Ecosystem compatibility

• ✅ Transformers v5 🤗 - Built on latest transformers with device-mesh driven parallelism.
• ✅ 🤗 HuggingFace Integration - Works with dense models (e.g., Qwen, Llama3, etc) and large MoEs (e.g., DSv3, DSv4).
• ✅ VLM - Finetuning for VLMs (Qwen2.5/3/3.5/3.6 VL, Gemma-3/3n/4 VL, Mistral 3.5/4, LLaVA-OneVision-1.5, Kimi-VL, etc.).
• ✅ Omnimodal - Finetuning for omnimodal MoE models (Nemotron-3-Nano-Omni, Qwen3-Omni).
• ✅ Diffusion - Pretraining and LoRA finetuning for image/video diffusion models (Qwen-Image, FLUX, Wan2.1, Wan2.2-T2V-A14B, Hunyuan).
• ✅ dLLM - Discrete diffusion LM finetuning (LLaDA, LLaDA2, Nemotron-Labs-Diffusion, DiffusionGemma).
• ✅ Retrieval - Bi-encoder and cross-encoder training with in-batch negative sampling.
• ✅ Extended MoE support - GPT-OSS, Kimi K3, Qwen3 / Qwen3.5 / Qwen3.6 MoE, Qwen-next, MiniMax-M2.x, GLM-4.7 / GLM-5 / GLM-5.1 / GLM-5.2 / GLM-5.3 / GLM-5.3-Flash, DeepSeek V3.2 / V4 / V4-Flash, ERNIE 4.5, MiMo-V2-Flash, Ling 2.0, Hy3-preview.

Agentic Development and UX

• ✅ Agent-friendly skills - Curated skills/ for common dev tasks (recipe runs, model onboarding, CI).

Planned for 26.08

• 🔜 Unified Engine API and recipes - Introduce a common engine and consolidate the LLM and VLM recipe paths.
• 🔜 Composable component configuration - Complete the typed config and .build() refactor across data and remaining components.
• 🔜 Packed long-context training with CP - Combine THD sequence packing with context parallelism, including DeepSeek V4 coverage.
• 🔜 Kernel and runtime upgrades - Add partial CUDA graphs, evaluate FlashAttention 3/4, and upgrade to DeepEP v2.
• 🔜 Multimodal retrieval - Expand vision-language retrieval datasets and models and improve retriever performance.
• 🔜 Convergence and regression testing - Add automated loss, evaluation, and performance gates to NeMo CI.
• 🔜 Transformers 5.12 alignment - Upgrade the Transformers integration and restore affected model parity coverage.
• 🔜 Recipe consistency - Align diffusion recipes with the shared AutoModel recipe and configuration patterns.

Getting Started

We recommend using uv for reproducible Python environments.

# Setup environment before running any recipes
uv venv

# Choose ONE:
uv sync --frozen  # LLM recipes (default)
# uv sync --frozen --extra vlm --extra vlm-media  # VLM recipes (Qwen/Mistral/Omni need vlm-media for video/vision; fixes: ImportError: qwen_vl_utils is not installed)
# uv sync --frozen --extra cuda  # Optional CUDA deps (e.g., Transformer Engine, Mamba SSM)
# uv sync --frozen --extra cuda_source  # Optional bitsandbytes dependency
# uv sync --frozen --extra all  # Most optional deps (includes `vlm` and `cuda`; NOTE: excludes media — add --extra media for video/image decode)
# uv sync --frozen --all-extras  # Everything (includes `fa`, `moe`, `media`, etc.)

# One-off runs (examples):
# uv run --extra vlm <command>
# uv run --extra cuda <command>

uv run python -c "import nemo_automodel; print('NeMo AutoModel ready')"

Run a Recipe

All recipes are launched via the automodel CLI (or its short alias am). Each YAML config specifies the recipe class and all training parameters:

# LLM example: multi-GPU fine-tuning with FSDP2
automodel examples/llm_finetune/llama3_2/llama3_2_1b_hellaswag.yaml --nproc-per-node 8

# VLM example: single-GPU fine-tuning (Gemma-3-VL) with LoRA
automodel examples/vlm_finetune/gemma3/gemma3_vl_4b_cord_v2_peft.yaml

# Both commands also work with uv run:
uv run automodel examples/llm_finetune/llama3_2/llama3_2_1b_hellaswag.yaml --nproc-per-node 8

[!TIP] NeMo-Run submission: The cli extra adds NeMo Run to the base package: uv pip install "nemo-automodel[cli]". It is additive; the base package still installs its core training dependencies, including PyTorch.

LLM Pre-training

LLM Pre-training Single Node

We provide an example SFT experiment using the FineWeb dataset with a nano-GPT model, ideal for quick experimentation on a single node.

automodel examples/llm_pretrain/nanogpt_pretrain.yaml --nproc-per-node 8

LLM Supervised Fine-Tuning (SFT)

We provide an example SFT experiment using the SQuAD dataset.

LLM SFT Single Node

The default SFT configuration is set to run on a single GPU. To start the experiment:

automodel examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml

This fine-tunes the Llama3.2-1B model on the SQuAD dataset using a single GPU.

To use multiple GPUs on a single node, add the --nproc-per-node argument:

automodel examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml --nproc-per-node 8

LLM SFT Multi Node

To launch on a SLURM cluster, copy the reference sbatch script and adapt it to your cluster:

cp slurm.sub my_cluster.sub
# Edit my_cluster.sub — change CONFIG, #SBATCH directives, container, mounts, etc.
sbatch my_cluster.sub

All cluster-specific settings (nodes, GPUs, partition, container, mounts) live in your sbatch script. NeMo-Run (nemo_run:) sections are also supported -- see our cluster guide for details.

LLM Parameter-Efficient Fine-Tuning (PEFT)

We provide a PEFT example using the HellaSwag dataset.

LLM PEFT Single Node

# Memory-efficient SFT with LoRA
automodel examples/llm_finetune/llama3_2/llama3_2_1b_hellaswag_peft.yaml

# Override any YAML parameter via the command line:
automodel examples/llm_finetune/llama3_2/llama3_2_1b_hellaswag_peft.yaml \
  --step_scheduler.local_batch_size 16

[!NOTE] Launching a multi-node PEFT example uses the same sbatch slurm.sub workflow as the SFT case above.

VLM Supervised Fine-Tuning (SFT)

We provide a VLM SFT example using Qwen2.5-VL for end-to-end fine-tuning on image-text data.

VLM SFT Single Node

# Qwen2.5-VL on 8 GPUs
automodel examples/vlm_finetune/qwen2_5/qwen2_5_vl_3b_rdr.yaml --nproc-per-node 8

VLM Parameter-Efficient Fine-Tuning (PEFT)

We provide a VLM PEFT (LoRA) example for memory-efficient adaptation with Gemma3 VLM.

VLM PEFT Single Node

# Gemma-3-VL PEFT on 8 GPUs
automodel examples/vlm_finetune/gemma3/gemma3_vl_4b_medpix_peft.yaml --nproc-per-node 8

Supported Models

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 small sample of ready-to-use families (train as-is or swap any compatible 🤗 causal LM), you can specify nearly any LLM/VLM model available on 🤗 hub:

[!NOTE] Check out more LLM and VLM examples. Any causal LM on Hugging Face Hub can be used with the base recipe template, just overwrite --model.pretrained_model_name_or_path <model-id> in the CLI or in the YAML config.

Performance

NeMo AutoModel achieves great training performance on NVIDIA GPUs. Below are highlights from our benchmark results:

For complete benchmark results including configuration details, see the Performance Summary.

🔌 Interoperability

• NeMo RL: Use AutoModel checkpoints directly as starting points for DPO/RM/GRPO pipelines.
• Hugging Face: Train any LLM/VLM from 🤗 without format conversion.
• Megatron Bridge: Optional conversions to/from Megatron formats for specific workflows.

🗂️ Project Structure

NeMo-Automodel/
├── cli/                            # `automodel` / `am` CLI entry-point
│   └── app.py
├── docker/                         # Container build files
├── docs/                           # Documentation and guides
├── examples/
│   ├── convergence/                # Convergence test configs
│   ├── diffusion/                  # Diffusion pretrain/finetune configs
│   ├── dllm_sft/                   # Discrete diffusion LM SFT configs
│   ├── dllm_generate/              # Discrete diffusion LM generation
│   ├── llm_benchmark/              # LLM benchmarking configs
│   ├── llm_finetune/               # LLM finetune YAML configs
│   ├── llm_kd/                     # LLM knowledge-distillation configs
│   ├── llm_pretrain/               # LLM pretrain configs
│   ├── llm_seq_cls/                # LLM sequence classification configs
│   ├── retrieval/                  # Bi-encoder / cross-encoder configs
│   ├── vlm_benchmark/              # VLM benchmarking configs
│   ├── vlm_finetune/               # VLM finetune configs
│   └── vlm_generate/               # VLM generation configs
├── nemo_automodel/
│   ├── _diffusers/                 # HF Diffusers integration (NeMoAutoDiffusionPipeline)
│   ├── _transformers/              # HF Transformers integration
│   ├── components/                 # Core library
│   │   ├── _peft/                  # PEFT implementations (LoRA, QLoRA)
│   │   ├── attention/              # Attention implementations
│   │   ├── checkpoint/             # Distributed checkpointing
│   │   ├── config/
│   │   ├── datasets/               # LLM, VLM, diffusion, retrieval datasets
│   │   ├── distributed/            # FSDP2, Megatron FSDP, pipelining, CP, etc.
│   │   ├── launcher/               # Launcher backends (SLURM, NeMo-Run, SkyPilot)
│   │   ├── loggers/                # Loggers
│   │   ├── loss/                   # Optimized loss functions
│   │   ├── models/                 # User-defined model examples
│   │   ├── moe/                    # Optimized kernels for MoE models
│   │   ├── optim/                  # Optimizer/LR scheduler components (incl. Dion)
│   │   ├── quantization/           # FP8, QAT, QLoRA
│   │   ├── training/               # Train utils
│   │   └── utils/                  # Misc utils
│   ├── recipes/
│   │   ├── llm/                    # Main LLM train loop
│   │   ├── vlm/                    # Main VLM train loop
│   │   ├── diffusion/              # Diffusion training loop
│   │   ├── dllm/                   # Discrete diffusion LM training loop
│   │   └── retrieval/              # Retrieval / biencoder training loop
│   └── shared/
├── tools/                          # Developer tooling
└── tests/                          # Comprehensive test suite

Citation

If you use NeMo AutoModel in your research, please cite it using the following BibTeX entry:

@misc{nemo-automodel,
title = {NeMo AutoModel: DTensor-native SPMD library for scalable and efficient training},
howpublished = {\url{https://github.com/NVIDIA-NeMo/Automodel}},
year = {2025--2026},
note = {GitHub repository},
}

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

NVIDIA NeMo AutoModel is licensed under the Apache License 2.0.