Search Results for author:
Found 7 papers, 5 papers with code
Nemotron-4 340B Technical Report
We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward.
Nemotron-4 15B Technical Report
We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens.
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
Next, we detail the training process, the design of our training corpus, and our data curation techniques, which we believe is a key ingredient to the success of the model.
Ranked #35 on
Language Modelling
on LAMBADA
Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM
In this paper, we show how different types of parallelism methods (tensor, pipeline, and data parallelism) can be composed to scale to thousands of GPUs and models with trillions of parameters.
Neural ODEs for Image Segmentation with Level Sets
We propose a novel approach for image segmentation that combines Neural Ordinary Differential Equations (NODEs) and the Level Set method.
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
To demonstrate that large language models can further advance the state of the art (SOTA), we train an 8. 3 billion parameter transformer language model similar to GPT-2 and a 3. 9 billion parameter model similar to BERT.
Ranked #2 on
Reading Comprehension
on RACE
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages.
