The blog introduces image captioning and provides hands-on tutorials on three different Transformer-based encoder-decoder image captioning models: ViT-GPT2, BLIP, and Alpha- CLIP, deployed on AMD GPUs using ROCm.

Learn how to use bitsandbytes’ 8-bit representations techniques, 8-bit optimizer and LLM.int8, to optimize your LLMs training and inference using ROCm on AMD GPUs

This blog demonstrates how to speed up the training of a ResNet model on the CIFAR-100 classification task using PyTorch DDP on AMD GPUs with ROCm.

Torchtune is a PyTorch library that enables efficient fine-tuning of LLMs. In this blog we use Torchtune to fine-tune the Llama-3.1-8B model for summarization tasks using LoRA and showcasing scalable training across multiple GPUs.

Optimizing Transformer models with CTranslate2 for efficient inference on AMD GPUs

Meta's Llama 3.2 Vision models bring multimodal capabilities for vision-text tasks. This blog explores leveraging them on AMD GPUs with ROCm for efficient AI workflows.

This blog will introduce you to assisted text generation using Speculative Sampling. We briefly explain the principles underlying Speculative Sampling and demonstrate its implementation on AMD GPUs using ROCm.

This blog demonstrates how to set-up and fine-tune a Stable Diffusion XL (SDXL) model in a multinode Oracle Cloud Infrastructure’s (OCI) Kubernetes Engine (OKE) on a cluster of AMD GPUs using ROCm

In this blog post we guide you through developing a fused dropout activation kernel for matrices in Triton, calling the kernel from JAX, and benchmarking its performance.

This blog demonstrates how to use AMD GPUs to implement and evaluate INT8 quantization, and the derived inference speed-up of Llama family and Mistral LLM models.

This blog demonstrates how to fine-tune Llama 3 with Axolotl using ROCm on AMD GPUs, and how to evaluate the performance of your LLM before and after fine-tuning.

Learn step-by-step how to leverage vLLM for high-performance inferencing and model serving on AMD GPUs