In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Yi models on Intel GPUs. For illustration purposes, we utilize the 01-ai/Yi-6B as a reference Yi model.
To run these examples with BigDL-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to here for more information.
In the example generate.py, we show a basic use case for a Yi model to predict the next N tokens using generate()
API, with BigDL-LLM INT4 optimizations on Intel GPUs.
We suggest using conda to manage the Python environment. For more information about conda installation, please refer to here.
After installing conda, create a Python environment for BigDL-LLM:
conda create -n llm python=3.9 # recommend to use Python 3.9
conda activate llm
# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
# you can install specific ipex/torch version for your need
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
pip install einops # additional package required for Yi-6B to conduct generation
source /opt/intel/oneapi/setvars.sh
For optimal performance on Arc, it is recommended to set several environment variables.
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
python ./generate.py
In the example, several arguments can be passed to satisfy your requirements:
--repo-id-or-model-path REPO_ID_OR_MODEL_PATH
: argument defining the huggingface repo id for the Yi model (e.g.01-ai/Yi-6B
) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be'01-ai/Yi-6B'
.--prompt PROMPT
: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be'AI是什么?'
.--n-predict N_PREDICT
: argument defining the max number of tokens to predict. It is default to be32
.
Inference time: xxxx s
-------------------- Prompt --------------------
AI是什么?
-------------------- Output --------------------
AI是什么?
人工智能(Artificial Intelligence),英文缩写为AI。它是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及