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Vicuna

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Vicuna models. For illustration purposes, we utilize the lmsys/vicuna-13b-v1.3 and eachadea/vicuna-7b-1.1 as reference Vicuna models.

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 Vicuna model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations.

1. Install

We suggest using conda to manage environment:

On Linux:

conda create -n llm python=3.11 # recommend to use Python 3.11
conda activate llm

# install the latest ipex-llm nightly build with 'all' option
pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu

On Windows:

conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[all]

2. Run

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 Vicuna model (e.g. lmsys/vicuna-13b-v1.3 and eachadea/vicuna-7b-1.1) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'lmsys/vicuna-13b-v1.3'.
  • --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 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 Vicuna 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

lmsys/vicuna-13b-v1.3

Inference time: xxxx s
-------------------- Prompt --------------------
### Human:
What is AI? 
 ### Assistant:

-------------------- Output --------------------
### Human:
What is AI? 
 ### Assistant:
AI, or Artificial Intelligence, refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception,

eachadea/vicuna-7b-1.1

Inference time: xxxx s
-------------------- Prompt --------------------
### Human:
What is AI? 
 ### Assistant:

-------------------- Output --------------------
### Human:
What is AI? 
 ### Assistant:
AI, or artificial intelligence, refers to the ability of a machine or computer program to mimic human intelligence and perform tasks that would normally require human intelligence to