Fine-tuning LLMs and inference optimization

2024-11-06

4 min read time

Applies to Linux

ROCm empowers the fine-tuning and optimization of large language models, making them accessible and efficient for specialized tasks. ROCm supports the broader AI ecosystem to ensure seamless integration with open frameworks, models, and tools.

For more information, see What is ROCm?

Throughout the following topics, this guide discusses the goals and challenges of fine-tuning a large language model like Llama 2. Then, it introduces common methods of optimizing your fine-tuning using techniques like LoRA with libraries like PEFT. In the sections that follow, you’ll find practical guides on libraries and tools to accelerate your fine-tuning.

• Conceptual overview of fine-tuning LLMs

• Fine-tuning and inference using a single-accelerator or multi-accelerator system.

• Model quantization

• Model acceleration libraries

• LLM inference frameworks

• Optimizing with Composable Kernel

• Optimizing Triton kernels

• Profiling and debugging