ipex-llm/python/llm/example/GPU/HF-Transformers-AutoModels/Model/aquila2

Aquila2

In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Aquila2 models. For illustration purposes, we utilize the BAAI/AquilaChat2-7B as a reference Aquila2 model.

Note

: If you want to download the Hugging Face Transformers model, please refer to here.

BigDL-LLM optimizes the Transformers model in INT4 precision at runtime, and thus no explicit conversion is needed.

Requirements

To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to here for more information.

Example: Predict Tokens using generate() API

In the example generate.py, we show a basic use case for a Aquila2 model to predict the next N tokens using generate() API, with BigDL-LLM INT4 optimizations.

1. Install

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

2. Configures OneAPI environment variables

source /opt/intel/oneapi/setvars.sh

3. Run

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 In the example, several arguments can be passed to satisfy your requirements:

• --repo-id-or-model-path: str, argument defining the huggingface repo id for the Aquila2 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'BAAI/AquilaChat2-7B'.
• --prompt: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be 'AI是什么？'.
• --n-predict: int, argument defining the max number of tokens to predict. It is default to be 32.

Sample Output

BAAI/AquilaChat2-7B

Inference time: xxxx s
-------------------- Prompt --------------------
<|startofpiece|>AI是什么？<|endofpiece|>
-------------------- Output --------------------
<|startofpiece|>AI是什么？<|endofpiece|>人工智能（Artificial Intelligence，简称AI）是计算机科学中一个极为重要的研究领域，旨在让计算机模仿人类的智能，包括学习、推理、识别物体