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+
+# NeMo Automodel
+
+[NeMo Automodel](https://github.com/NVIDIA-NeMo/Automodel) is an open-source PyTorch DTensor-native training library from NVIDIA. It supports large and small scale pretraining and fine-tuning for [LLMs](https://docs.nvidia.com/nemo/automodel/latest/model-coverage/llm.html) and [VLMs](https://docs.nvidia.com/nemo/automodel/latest/model-coverage/vlm.html) for fast experimentation in research and production environments, with parallelism strategies including FSDP2, tensor, pipeline, expert, and context parallelism. For high throughput, it integrates kernels from DeepEP and TransformerEngine. 
+
+```py
+# Instantiating Nemotron v3 Nano with expert parallelism, FSDP, and TransformerEngine + DeepEP kernels.
+import os
+
+import torch
+import torch.distributed as dist
+
+from nemo_automodel import NeMoAutoModelForCausalLM
+from nemo_automodel.recipes._dist_setup import setup_distributed
+
+dist.init_process_group(backend="nccl")
+torch.cuda.set_device(int(os.environ.get("LOCAL_RANK", 0)))
+torch.manual_seed(1111)
+
+dist_setup = setup_distributed(
+    {
+        "strategy": "fsdp2",
+        "dp_size": None,  # will be inferred from world_size and other parallelism sizes
+        "dp_replicate_size": None,
+        "tp_size": 1,
+        "pp_size": 1,
+        "cp_size": 1,
+        "ep_size": 8,
+    },
+    world_size=dist.get_world_size(),
+)
+kwargs = {
+    "device_mesh": dist_setup.device_mesh,
+    "moe_mesh": dist_setup.moe_mesh,
+    "distributed_config": dist_setup.strategy_config,
+    "moe_config": dist_setup.moe_config,
+}
+model = NeMoAutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", **kwargs)
+print(model)
+dist.destroy_process_group()
+```
+Launch the script with `torchrun` using the command below.
+
+```bash
+torchrun --nproc-per-node=8 /path/to/script
+```
+
+## Transformers integration
+
+- Any LLM or VLM supported in Transformers can also be instantiated through NeMo Automodel. See the [full model coverage](https://docs.nvidia.com/nemo/automodel/latest/model-coverage/overview.html).
+- Built on top of Hugging Face models with [`AutoModel.from_pretrained`], with dynamic high-performance layer swaps and support for more refined parallelisms like Expert Parallelism (EP).
+- Detects the architecture field in [`AutoConfig.from_pretrained`] to automatically load custom implementations like Nemotron Nano V3.
+- Follows the Transformers API closely for drop-in compatibility.
+
+## Resources
+
+- [NeMo Automodel](https://github.com/NVIDIA-NeMo/Automodel)
+- [NeMo Transformers API](https://docs.nvidia.com/nemo/automodel/latest/guides/huggingface-api-compatibility.html)
+- NeMo Automodel dense models and Mixture-of-Expert (MoE) [benchmarks](https://docs.nvidia.com/nemo/automodel/latest/performance-summary.html)
+- See the NeMo [fine-tuning](./nemo_automodel_finetuning) guide to learn how to use NeMo for fine-tuning