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# Dynamic Quantization for Llama-2-7b

This folder contains an example of quantizing an Llama-2-7b model using the ONNX quantizer of Quark. The example has the following parts:

- Pip Requirements

- Prepare Model

- Quantization

- Evaluation

## Pip requirements

Install the necessary python packages:

    python -m pip install -r requirements.txt

## Prepare model

Download the HF Llama-2-7b-hf checkpoint. The Llama2 models checkpoint can be accessed by submitting a permission request to Meta. For additional details, see the Llama2 page on Huggingface. Upon obtaining permission, download the checkpoint to the Llama-2-7b-hf folder.

Export onnx model from Llama-2-7b-hf torch model:

    mkdir models && optimum-cli export onnx --model ./Llama-2-7b-hf --task text-generation ./models/

## Quantization

The quantizer takes the float model and produces a dynamically quantized model:

    cp -r models dynamic_quantized_models
    find dynamic_quantized_models -type f -name '*onnx*' -exec rm -f {} \;
    python quantize_model.py --input_model_path models/model.onnx \
                             --output_model_path dynamic_quantized_models/model.onnx \
                             --config UINT8_DYNAMIC_QUANT

This command will generate a quantized model under the dynamic_quantized_models folder, using the UInt8 dynamic quantization configuration.

## Evaluation

Test the PPL of the float model on wikitext2.raw:

    python onnx_validate.py --model_name_or_path models/ --per_gpu_eval_batch_size 1 --block_size 2048 --do_onnx_eval --no_cuda

Test the PPL of the dynamic quantized model:

    python onnx_validate.py --model_name_or_path dynamic_quantized_models/ --per_gpu_eval_batch_size 1 --block_size 2048 --do_onnx_eval --no_cuda

| Float Model | Dynamic Quantized Model
--- | --- | ---
Model Size | 26.0 G | 6.4 G
PPL | 5.6267 | 8.8859