deciLM-7b

DeciLM-7B

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on DeciLM-7B models. For illustration purposes, we utilize the Deci/DeciLM-7B-instruct as a reference DeciLM-7B 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 DeciLM-7B 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
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
pip install transformers==4.35.2 # required by DeciLM-7B

On Windows:

conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[all]
pip install transformers==4.35.2

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 DeciLM-7B model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'Deci/DeciLM-7B-instruct'.
• --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 DeciLM-7B 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

Deci/DeciLM-7B-instruct

Inference time: XXXX s
-------------------- Prompt --------------------
### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
### User:
What is AI?
### Assistant:
-------------------- Output --------------------
### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
### User:
What is AI?
### Assistant:
 AI stands for Artificial Intelligence, which refers to the development of computer systems and software that can perform tasks that typically require human intelligence, such as recognizing patterns