[OMNIML-2244] Add E2E example for mixed precision quantization and ONNX export

[ajrasane]

What does this PR do?

Type of change:
New Feature

Overview:

• Enable ONNX export for auto quantized models
• Update documentation and changelog

Usage

python torch_quant_to_onnx.py --quantize_mode=auto \
	--onnx_save_path=./vit_base_patch16_224.nvfp4_fp8.onnx \
	--calibration_data_size 64 \
	--auto_quantization_formats NVFP4_AWQ_LITE_CFG FP8_DEFAULT_CFG \
	--batch_size 128

Testing

python evaluate.py --onnx_path=vit_base_patch16_224.nvfp4_fp8.onnx \
	--model_name=vit_base_patch16_224 \
	--results_path=./results.txt \
	--batch_size 128

Accuracy results

The top1 accuracy of the model is 84.15%
The top5 accuracy of the model is 97.396%

Reference accuracy for fp16

The top1 accuracy of the model is 85.102%
The top5 accuracy of the model is 97.526%

Before your PR is "Ready for review"

• Make sure you read and follow Contributor guidelines and your commits are signed.
• Is this change backward compatible?: Yes
• Did you write any new necessary tests?: No
• Did you add or update any necessary documentation?: Yes
• Did you update Changelog?: Yes

[codecov]

Codecov Report

❌ Patch coverage is 0% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 74.50%. Comparing base (3ef9e39) to head (23e00a2).
⚠️ Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
modelopt/torch/_deploy/utils/torch_onnx.py	0.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #656      +/-   ##
==========================================
- Coverage   74.57%   74.50%   -0.07%     
==========================================
  Files         183      183              
  Lines       18451    18400      -51     
==========================================
- Hits        13759    13709      -50     
+ Misses       4692     4691       -1     

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