Save-Load

Save/Load Low-Bit Models with IPEX-LLM Optimizations

In this example, we show how to save/load model with IPEX-LLM low-bit optimizations (including INT8/INT5/INT4), and then run inference on the optimized low-bit model.

0. Requirements

To run this example with IPEX-LLM, we have some recommended requirements for your machine, please refer to here for more information.

Example: Save/Load Model in Low-Bit Optimization

In the example generate.py, we show a basic use case of saving/loading model in low-bit optimizations to predict the next N tokens using generate() API. Also, saving and loading operations are platform-independent, so you could run it on different platforms.

1. Install

We suggest using conda to manage environment:

conda create -n llm python=3.11
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

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

If you want to save the optimized low-bit model, run:

python ./generate.py --save-path path/to/save/model

If you want to load the optimized low-bit model, run:

python ./generate.py --load-path path/to/load/model

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

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the Llama2 model (e.g. meta-llama/Llama-2-7b-chat-hf and meta-llama/Llama-2-13b-chat-hf) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'meta-llama/Llama-2-7b-chat-hf'.
• --low-bit: argument defining the low-bit optimization data type, options are sym_int4, asym_int4, sym_int5, asym_int5 or sym_int8. (sym_int4 means symmetric int 4, asym_int4 means asymmetric int 4, etc.). Relevant low bit optimizations will be applied to the model.
• --save-path: argument defining the path to save the low-bit model. Then you can load the low-bit directly.
• --load-path: argument defining the path to load low-bit model.
• --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.

3 Sample Output

meta-llama/Llama-2-7b-chat-hf

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

### RESPONSE:

AI is a term used to describe the development of computer systems that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing images

meta-llama/Llama-2-13b-chat-hf

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

### RESPONSE:

AI, or Artificial Intelligence, refers to the ability of machines to perform tasks that would typically require human intelligence, such as learning, problem-