How do you fine-tune a model using onnix

To fine-tune a model using ONNX, you can use the `onnx_train` command in the ONNX Runtime. Here's an example of how to do it:

bash

# First, make sure you have the ONNX Runtime installed and configured on your system. You can install it using pip:

# $ pip install onnx-runtime

# Next, create a new ONNX model file for fine-tuning. For example, let's say you want to fine-tune a pre-trained image classification model on the CIFAR-10 dataset. You can use the following command to create a new ONNX model file:

# $ onnx_train --model_name cifar_classifier --input_type float32 --output_type float32 --data_type float32 --num_classes 10 --image_size 32x32 --batch_size 32 --learning_rate 0.001 --epochs 10 --checkpoint_path cifar_classifier.onnx

# This command will create a new ONNX model file called "cifar\_classifier.onnx" and fine-tune the pre-trained image classification model on the CIFAR-10 dataset for 10 epochs with a learning rate of 0.001. The model will be saved to the specified checkpoint path.

Note that this is just an example, and you may need to modify the command based on your specific use case and dataset.