Posts by Yusheng Su
Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration
- 24 April 2025
In this blog post, we provide an overview of Volcano Engine Reinforcement Learning for LLMs (verl) and discuss its benefits in large-scale reinforcement learning from human feedback (RLHF). We also detail the modifications made to the codebase to optimize verl’s performance on AMD Instinct GPUs. Next, we walk through the process of building the Docker image using a Dockerfile on the user side, along with training scripts tailored for both single-node and multi-node setups. Lastly, we present verl’s performance results, focusing on throughput and convergence accuracy achieved on AMD Instinct™ MI300X GPUs. Follow this guide to get started with verl on AMD Instinct GPUs and accelerate your RLHF training with ROCm-optimized performance.
Instella-VL-1B: First AMD Vision Language Model
- 07 March 2025
As part of AMD’s newly released Instella family we are thrilled to introduce Instella-VL-1B, the first AMD vision language model for image understanding trained on AMD Instinct™ MI300X GPUs. Our journey with Instella-VL builds upon our previous 1-billion-parameter language models, AMD OLMo SFT. We further extend the language model’s visual understanding abilities by connecting it with a vision encoder (which is initialized from CLIP ViT-L/14-336). During training, we jointly finetune vision encoder and language model with vision-language data in three stages: Alignment, Pretraining and Supervised-Finetuning (SFT).
Introducing Instella: New State-of-the-art Fully Open 3B Language Models
- 05 March 2025
AMD is excited to announce Instella, a family of fully open state-of-the-art 3-billion-parameter language models (LMs) trained from scratch on AMD Instinct™ MI300X GPUs. Instella models outperform existing fully open models of similar sizes and achieve competitive performance compared to state-of-the-art open-weight models such as Llama-3.2-3B, Gemma-2-2B, and Qwen-2.5-3B, including their instruction-tuned counterparts.