Large Language Models Weight Compression Example

This example demonstrates how to optimize Large Language Models (LLMs) using NNCF weight compression API. The example applies 4/8-bit mixed-precision quantization to weights of Linear (Fully-connected) layers of TinyLlama/TinyLlama-1.1B-Chat-v1.0 model after converting it into a TorchFX representation. This leads to a significant decrease in model footprint and performance improvement with OpenVINO.

Prerequisites

To use this example:

• Create a separate Python* environment and activate it: python3 -m venv nncf_env && source nncf_env/bin/activate
• Install dependencies:

pip install -U pip
pip install -r requirements.txt
pip install ../../../../

Run Example

To run example:

python main.py

It will automatically download the dataset and baseline model then run the model with a sample prompt.