Applications & models - Page 18

Applications & models - Page 18#

Explore the latest blogs about applications and models in the ROCm ecosystem, including machine learning frameworks, AI models, and application case studies.

April 09, 2024

ResNet for image classification using AMD GPUs

./artificial-intelligence/resnet/README.html

April 08, 2024

Small language models with Phi-2

./artificial-intelligence/Phi2/README.html

April 04, 2024

Retrieval Augmented Generation (RAG) using LlamaIndex

./artificial-intelligence/rag-llamaindex/README.html

April 04, 2024

Using the ChatGLM-6B bilingual language model with AMD GPUs

Bilingual language model ChatGLM-6B

./artificial-intelligence/ChatGLM/README.html

April 04, 2024

Building semantic search with SentenceTransformers on AMD

./artificial-intelligence/sentence_transformers_amd/README.html

April 04, 2024

Total body segmentation using MONAI Deploy on an AMD GPU

Total body segmentation using MONAI Deploy with ROCm

./artificial-intelligence/monai-deploy/README.html

April 04, 2024

Image classification using Vision Transformer with AMD GPUs

./artificial-intelligence/vit-inference/README.html

April 01, 2024

Scale AI applications with Ray

./artificial-intelligence/ray/README.html

March 29, 2024

Automatic mixed precision in PyTorch using AMD GPUs

In this blog, we will discuss the basics of AMP, how it works, and how it can improve training efficiency on AMD GPUs. As models increase in size, the time and memory needed to train them--and consequently, the cost--also increases. Therefore, any measures we take to reduce training time and memory usage can be highly beneficial. This is where Automatic Mixed Precision (AMP) comes in.

./artificial-intelligence/automatic-mixed-precision/README.html

March 15, 2024