Publications (5)
TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training
Wanchao Liang, Tianyu Liu, Less Wright +10
The development of large language models (LLMs) has been instrumental in advancing state-of-the-art natural language processing applications. Training LLMs with billions of paramet…
TorchBench: Benchmarking PyTorch with High API Surface Coverage
Yueming Hao, Xu Zhao, Bin Bao +4
Deep learning (DL) has been a revolutionary technique in various domains. To facilitate the model development and deployment, many deep learning frameworks are proposed, among whic…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
Using Python for Model Inference in Deep Learning
Zachary DeVito, Jason Ansel, Will Constable +3
Python has become the de-facto language for training deep neural networks, coupling a large suite of scientific computing libraries with efficient libraries for tensor computation…
Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning
Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla +18
The Deep Learning (DL) community sees many novel topologies published each year. Achieving high performance on each new topology remains challenging, as each requires some level of…