qwen

Qwen

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Qwen models. For illustration purposes, we utilize the Qwen-7B-Chat as a reference Qwen model.

0. Requirements

To run these examples with IPEX-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 Qwen model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations.

1. Install

We suggest using conda to manage environment:

conda create -n llm python=3.11
conda activate llm

pip install ipex-llm[all] # install ipex-llm with 'all' option
pip install tiktoken einops transformers_stream_generator  # additional package required for Qwen-7B-Chat to conduct generation

2. Run

The minimum Qwen model version currently supported by IPEX-LLM is the version on November 30, 2023.

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 Qwen model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'Qwen/Qwen-7B-Chat'.
• --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 be 32.

Note: When loading the model in 4-bit, IPEX-LLM converts linear layers in the model into INT4 format. In theory, a XB model saved in 16-bit will requires approximately 2X GB of memory for loading, and ~0.5X GB memory for further inference.

Please select the appropriate size of the Qwen model based on the capabilities of your machine.

2.1 Client

On client Windows machine, it is recommended to run directly with full utilization of all cores:

python ./generate.py 

2.2 Server

For optimal performance on server, it is recommended to set several environment variables (refer to here for more information), and run the example with all the physical cores of a single socket.

E.g. on Linux,

# set IPEX-LLM env variables
source ipex-llm-init

# e.g. for a server with 48 cores per socket
export OMP_NUM_THREADS=48
numactl -C 0-47 -m 0 python ./generate.py

2.3 Sample Output

Qwen/Qwen-7B-Chat

Inference time: xxxx s
-------------------- Prompt --------------------
<human>AI是什么？ <bot>
-------------------- Output --------------------
<human>AI是什么？ <bot>AI，也称为人工智能，是指计算机科学的一个分支，其目标是创造出能够执行某些任务的智能机器。AI的研究涵盖了机器学习、深度学习

Inference time: xxxx s
-------------------- Prompt --------------------
<human>What is AI? <bot>
-------------------- Output --------------------
<human>What is AI? <bot>AI stands for Artificial Intelligence. It refers to the ability of a computer program or machine to perform tasks that typically require