README.md

Qwen2.5-1.5B-Instruct Mixed Precision Quantization

This recipe demonstrates how to use Olive to perform mixed precision (INT4/INT4) quantization, export to ONNX, and evaluate using lm-evaluation-harness. Please refer to the Exploring Optimal Quantization Settings for Small Language Models with Olive for more details.

Pre-requisites

Install Olive and other dependencies:

pip install -r requirements.txt

Run

To run the mixed precision quantization recipe, execute the following command:

olive run --config mixed.json

Note: Evaluation requires a machine with CUDA enabled GPU. If you don't have a GPU, you can skip the evaluation step by modifying the mixed.json file to remove the "evaluator": "evaluator" line.

Results

model	arc_challenge	arc_easy	mmlu	hellaswag	mmlu_stem	openbookqa	model_size_gb
Original (fp16)	0.465	0.760	0.601	0.683	0.539	0.404	3.318
Mixed	0.487	0.772	0.592	0.670	0.533	0.410	1.479