ipex-llm/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ

AWQ

This example shows how to directly run 4-bit AWQ models using IPEX-LLM on Intel CPU.

Verified Models

Auto-AWQ Backend

• Llama-2-7B-Chat-AWQ
• CodeLlama-7B-AWQ
• Mistral-7B-Instruct-v0.1-AWQ
• Mistral-7B-v0.1-AWQ
• vicuna-7B-v1.5-AWQ
• vicuna-13B-v1.5-AWQ
• llava-v1.5-13B-AWQ
• Yi-6B-AWQ
• Yi-34B-AWQ
• Mixtral-8x7B-Instruct-v0.1-AWQ

llm-AWQ Backend

• vicuna-7b-1.5-awq

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 AWQ 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

pip install autoawq==0.1.8 --no-deps
# install ipex-llm with 'all' option
pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
pip install transformers==4.35.0
pip install accelerate==0.25.0
pip install einops

On Windows:

conda create -n llm python=3.11
conda activate llm

pip install autoawq==0.1.8 --no-deps
pip install --pre --upgrade ipex-llm[all]
pip install transformers==4.35.0
pip install accelerate==0.25.0
pip install einops

Note: For Mixtral model, please use transformers 4.36.0:

pip install transformers==4.36.0

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 AWQ model (e.g. TheBloke/Llama-2-7B-Chat-AWQ, TheBloke/Mistral-7B-Instruct-v0.1-AWQ, TheBloke/Mistral-7B-v0.1-AWQ) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'TheBloke/Llama-2-7B-Chat-AWQ'.
• --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 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

TheBloke/Llama-2-7B-Chat-AWQ

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

### RESPONSE:

-------------------- Output --------------------
### HUMAN:
What is AI?

### RESPONSE:

Artificial intelligence (AI) is the ability of machines to perform tasks that typically require human intelligence, such as learning, problem-solving, decision