ipex-llm/python/llm/example/GPU
2023-11-27 11:04:27 +08:00
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Deepspeed-AutoTP Add deepspeed autotp example readme (#9289) 2023-10-27 13:04:38 -07:00
HF-Transformers-AutoModels LLM: support Mistral AWQ models (#9520) 2023-11-24 16:20:22 +08:00
PyTorch-Models Add examples for Yi-6B (#9421) 2023-11-13 10:53:15 +08:00
QLoRA-FineTuning LLM: quick fix alpaca qlora finetuning script (#9534) 2023-11-27 11:04:27 +08:00
README.md Update GPU example README (#9524) 2023-11-23 21:20:26 +08:00

BigDL-LLM Examples on Intel GPU

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

  • HF-Transformers-AutoModels: running any Hugging Face Transformers model on BigDL-LLM (using the standard AutoModel APIs)
  • QLoRA-FineTuning: running QLoRA finetuning using BigDL-LLM on Intel GPUs
  • Deepspeed-AutoTP: running distributed inference using DeepSpeed AutoTP (with BigDL-LLM low-bit optimized models) on Intel GPUs
  • PyTorch-Models: running any PyTorch model on BigDL-LLM (with "one-line code change")

System Support

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)

Requirements

To apply Intel GPU acceleration, therere several steps for tools installation and environment preparation.

Step 1, please refer to our driver installation for general purpose GPU capabilities.

Note

: IPEX 2.0.110+xpu requires Intel GPU Driver version is Stable 647.21.

Step 2, you also need to download and install Intel® oneAPI Base Toolkit. OneMKL and DPC++ compiler are needed, others are optional.

Note

: IPEX 2.0.110+xpu requires Intel® oneAPI Base Toolkit's version >= 2023.2.0.