ipex-llm/python/llm/example/GPU

BigDL-LLM Examples on Intel GPU

This folder contains examples of running BigDL-LLM on Intel GPU:

• Applications: running LLM applications (such as autogen) on BigDL-LLM
• HF-Transformers-AutoModels: running any Hugging Face Transformers model on BigDL-LLM (using the standard AutoModel APIs)
• LLM-Finetuning: running finetuning (such as LoRA, QLoRA, QA-LoRA, etc) using BigDL-LLM on Intel GPUs
• vLLM-Serving: running vLLM serving framework on intel GPUs (with BigDL-LLM low-bit optimized models)
• Deepspeed-AutoTP: running distributed inference using DeepSpeed AutoTP (with BigDL-LLM low-bit optimized models) on Intel GPUs
• LangChain: running LangChain applications on BigDL-LLM
• PyTorch-Models: running any PyTorch model on BigDL-LLM (with "one-line code change")
• Speculative-Decoding: running any Hugging Face Transformers model with self-speculative decoding on Intel GPUs
• ModelScope-Models: running ModelScope model with BigDL-LLM on Intel GPUs

System Support

1. Linux:

Hardware:

• Intel Arc™ A-Series Graphics
• Intel Data Center GPU Flex Series
• Intel Data Center GPU Max Series

Operating System:

• Ubuntu 20.04 or later (Ubuntu 22.04 is preferred)

2. Windows

Hardware:

• Intel iGPU and dGPU

Operating System:

• Windows 10/11, with or without WSL

Requirements

To apply Intel GPU acceleration, there’re several steps for tools installation and environment preparation. See the GPU installation guide for mode details.