Update docs/source/en/training/nemo_automodel.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

Author: pthombre

docs/source/en/training/nemo_automodel.md

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # NeMo Automodel
 
-[NeMo Automodel](https://github.com/NVIDIA-NeMo/Automodel) is a PyTorch DTensor-native training library from NVIDIA for fine-tuning and pretraining diffusion models at scale. It is **Hugging Face native** — train any Diffusers-format model from the Hub with no checkpoint conversion. The same YAML recipe and hackable training script runs on **any scale**, from 1 GPU to hundreds of nodes, with [FSDP2](https://pytorch.org/docs/stable/fsdp.html) distributed training, multiresolution bucketed dataloading, and pre-encoded latent space training for **maximum GPU utilization**. It uses [flow matching](https://huggingface.co/papers/2210.02747) for training and is fully open source (Apache 2.0), NVIDIA-supported, and actively maintained.
+[NeMo Automodel](https://github.com/NVIDIA-NeMo/Automodel) is a PyTorch DTensor-native training library from NVIDIA for fine-tuning and pretraining diffusion models at scale. It is Hugging Face native — train any Diffusers-format model from the Hub with no checkpoint conversion. The same YAML recipe and hackable training script runs on any scale from 1 GPU to hundreds of nodes, with [FSDP2](https://pytorch.org/docs/stable/fsdp.html) distributed training, multiresolution bucketed dataloading, and pre-encoded latent space training for maximum GPU utilization. It uses [flow matching](https://huggingface.co/papers/2210.02747) for training and is fully open source (Apache 2.0), NVIDIA-supported, and actively maintained.
 
 NeMo Automodel integrates directly with Diffusers. It loads pretrained models from the Hugging Face Hub using Diffusers model classes and generates outputs with the [`DiffusionPipeline`].