In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Chinese LLaMA models on Intel GPUs. For illustration purposes, we utilize the LinkSoul/Chinese-Llama-2-7b as reference Chinese LLaMA models.
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 Llama2 model to predict the next N tokens using generate()
API, with BigDL-LLM INT4 optimizations on Intel GPUs.
We suggest using conda to manage environment:
conda create -n llm 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
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 --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
Arguments info:
--repo-id-or-model-path REPO_ID_OR_MODEL_PATH
: argument defining the huggingface repo id for the Chinese Llama2 model (e.g.LinkSoul/Chinese-Llama-2-7b
) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be'LinkSoul/Chinese-Llama-2-7b'
.--prompt PROMPT
: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be'What is AI?'
.--n-predict N_PREDICT
: argument defining the max number of tokens to predict. It is default to be32
.
Inference time: xxxx s
-------------------- Prompt --------------------
<s>[INST] <<SYS>>
<</SYS>>
AI是什么? [/INST]
-------------------- Output --------------------
[INST] <<SYS>>
<</SYS>>
AI是什么? [/INST] AI(人工智能)是一种计算机科学,旨在开发能够模拟人