Recent Posts - Page 29#
Enhancing LLM Accessibility: A Deep Dive into QLoRA Through Fine-tuning Llama Model on a single AMD GPU
This blog demonstrate how to use QLora to efficiently fine-tune Llama model on a single AMD GPU with ROCm.
Developing Triton Kernels on AMD GPUs
This blog shows users how to develop and benchmark a custom Triton kernel
GPU Unleashed: Training Reinforcement Learning Agents with Stable Baselines3 on an AMD GPU in Gymnasium Environment
GPU Unleashed: Training Reinforcement Learning Agents with Stable Baselines3 on an AMD GPU in Gymnasium Environment
ResNet for image classification using AMD GPUs
ResNet for image classification using AMD GPUs
Retrieval Augmented Generation (RAG) using LlamaIndex
Retrieval Augmented Generation (RAG) using LlamaIndex
Using the ChatGLM-6B bilingual language model with AMD GPUs
Bilingual language model ChatGLM-6B
Building semantic search with SentenceTransformers on AMD
Building semantic search with SentenceTransformers on AMD
Total body segmentation using MONAI Deploy on an AMD GPU
Total body segmentation using MONAI Deploy with ROCm
Image classification using Vision Transformer with AMD GPUs
Image classification using Vision Transformer with AMD GPUs
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.