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Displaying 78 results

nemo-rl
NVIDIA
NVIDIA NeMo™ RL accelerates reinforcement learning post-training with high-performance GPU backends, offering scalable GRPO, DPO, SFT, and distillation for multimodal models from single-node experiments to enterprise-scale clusters.
Container
nemo-automodel
NVIDIA
NVIDIA NeMo™ AutoModel accelerates LLM and VLM training and fine‑tuning with PyTorch DTensor‑native SPMD, day‑0 Hugging Face support, and optimized parallelism from single‑ to multi‑node scale.
Container
GPUNet-0 pretrained weights (PyTorch, AMP, ImageNet)
NVIDIA Deep Learning Examples
GPUNet-0 ImageNet pretrained weights
Model
GPUNet-P0 pretrained weights (PyTorch, AMP, ImageNet)
NVIDIA Deep Learning Examples
GPUNet-P0 ImageNet pretrained weights
Model
GPUNet-D2 pretrained weights (PyTorch, AMP, ImageNet)
NVIDIA Deep Learning Examples
GPUNet-D2 weights pretrained on ImageNet
Model
GPUNet-1 pretrained weights (PyTorch, AMP, ImageNet)
NVIDIA Deep Learning Examples
GPUNet-1 ImageNet pretrained weights
Model
GPUNet-2 pretrained weights (PyTorch, AMP, ImageNet)
NVIDIA Deep Learning Examples
GPUNet-2 ImageNet pretrained weights
Model
GPUNet-D1 pretrained weights (PyTorch, AMP, ImageNet)
NVIDIA Deep Learning Examples
GPUNet-D1 weights pretrained on ImageNet
Model
GPUNet-P1 pretrained weights (PyTorch, AMP, ImageNet)
NVIDIA Deep Learning Examples
GPUNet-P1 ImageNet pretrained weights
Model
HiFi-GAN PyT checkpoint (22kHz, AMP)
NVIDIA Deep Learning Examples
HiFi-GAN v1 PyTorch checkpoint trained on 8GPU with AMP on LJSpeech-1.1 (22kHz).
Model
HiFi-GAN PyT checkpoint (FastPitch ftune, 22kHz, AMP)
NVIDIA Deep Learning Examples
HiFi-GAN v1 PyTorch checkpoint trained on 8GPU with AMP on LJSpeech-1.1 (22kHz), fine-tuned on FastPitch outputs.
Model
QuartzNet checkpoint (PyTorch, AMP, LibriSpeech)
NVIDIA Deep Learning Examples
QuartzNet PyTorch checkpoint trained on LibriSpeech (test-other 10.41% WER)
Model
Fine-tuning Flowtron Model
NVIDIA
Fine-tuning and inference the Flowtron model which is an auto-regressive flow-based generative network for text to speech synthesis with control over speech variation and style transfer.
Resource
TFT PyT checkpoint (Base, AMP, Electricity)
NVIDIA Deep Learning Examples
TFT Base PyTorch checkpoint trained with AMP on Electricity dataset
Model
Mask R-CNN TensorFlow checkpoint (AMP)
NVIDIA Deep Learning Examples
Mask R-CNN TensorFlow checkpoint trained with AMP
Model
EfficientNet For Tensorflow2
NVIDIA
EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, being an order-of-magnitude smaller and faster.
Resource
BERT PyTorch checkpoint (Dist-4L-288D, SQuAD1, seqLen384, AMP)
NVIDIA Deep Learning Examples
BERT Distilled 4L-288D PyTorch checkpoint distilled on SQuAD v1.1 dataset using AMP
Model
ResNet50 checkpoints (PaddlePaddle, AMP, ImageNet)
NVIDIA Deep Learning Examples
ResNet50 checkpoint trained with AMP on ImageNet
Model
BERT PyTorch checkpoint (Large, SST-2, seqLen128, AMP)
NVIDIA Deep Learning Examples
BERT Large PyTorch checkpoint finetuned on GLUE/SST-2 dataset using AMP
Model
TFT PyT checkpoint (Base, AMP, Traffic)
NVIDIA Deep Learning Examples
TFT Base PyTorch checkpoint trained with AMP on Traffic dataset
Model
FastPitch checkpoint (PyTorch, AMP, LJSpeech-1.1, 22050Hz)
NVIDIA Deep Learning Examples
FastPitch PyTorch checkpoint trained on LJSpeech-1.1
Model
BERT PyTorch checkpoint (Dist-6L-768D, SQuAD1, AMP)
NVIDIA Deep Learning Examples
BERT Distilled 6L-768D PyTorch checkpoint distilled on SQuAD v1.1 dataset using AMP
Model
Tacotron2 and WaveGlow PyTorch codebase
NVIDIA Deep Learning Examples
PyTorch codebase for training and using Tacotron2 and Waveglow models
Resource
BERT PyTorch checkpoint (Dist-6L-768D, Pretraining, AMP)
NVIDIA Deep Learning Examples
BERT Distilled 6L-768D PyTorch Phase2 checkpoint pretrained using 67K steps on seqLen128 and 6k steps on seqLen512
Model
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