Update --int8 flag to --optimized flag

[XuehaoSun]

Type of Change

feature or bug fix or documentation or validation or others
API changed or not

Description

detail description

Expected Behavior & Potential Risk

the expected behavior that triggered by this PR

How has this PR been tested?

how to reproduce the test (including hardware information)

Dependency Change?

any library dependency introduced or removed

[Copilot]

Pull Request Overview

This PR updates the command-line flag from "--int8" to "--optimized" to better reflect the behavior of loading optimized models rather than strictly int8-quantized ones.

• Updated flag references in model loading and optimization logic across multiple demonstration files.
• Modified printed messages and argument help text to align with the new flag nomenclature.

Reviewed Changes

Copilot reviewed 12 out of 24 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
examples/3.x_api/pytorch/recommendation/dlrm/static_quant/ipex/dlrm_s_pytorch.py	Replaced "int8" with "optimized" in optimization conditions.
examples/3.x_api/pytorch/nlp/huggingface_models/question-answering/quantization/static_quant/ipex/run_qa.py	Updated flag argument and printed message accordingly.
examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/pt2e/run_clm_no_trainer.py	Renamed flag usage for loading models.
examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/ipex/run_clm_no_trainer.py	Updated flag checks and printed messages for model loading.
examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/smooth_quant/run_clm_no_trainer.py	Changed the argument flag and associated model load condition.
examples/3.x_api/pytorch/image_recognition/torchvision_models/quantization/static_quant/ipex/main.py	Adjusted flag conditionals and printed messages in the main function.
examples/3.x_api/pytorch/diffusion_model/diffusers/stable_diffusion/smooth_quant/main.py	Modified the flag and condition for loading quantized models.
examples/3.x_api/pytorch/cv/static_quant/main.py	Updated the flag and condition used for model evaluation.

Files not reviewed (12)

• examples/3.x_api/pytorch/cv/static_quant/run_benchmark.sh: Language not supported
• examples/3.x_api/pytorch/diffusion_model/diffusers/stable_diffusion/smooth_quant/run_benchmark.sh: Language not supported
• examples/3.x_api/pytorch/image_recognition/torchvision_models/quantization/static_quant/ipex/run_benchmark.sh: Language not supported
• examples/3.x_api/pytorch/multimodal-modeling/quantization/auto_round/run_benchmark.sh: Language not supported
• examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/fp8_quant/run_benchmark.sh: Language not supported
• examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/smooth_quant/run_benchmark.sh: Language not supported
• examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/ipex/run_benchmark.sh: Language not supported
• examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/pt2e/run_benchmark.sh: Language not supported
• examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/transformers/weight_only/text-generation/run_benchmark.sh: Language not supported
• examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/weight_only/run_benchmark.sh: Language not supported
• examples/3.x_api/pytorch/nlp/huggingface_models/question-answering/quantization/static_quant/ipex/run_benchmark.sh: Language not supported
• examples/3.x_api/pytorch/recommendation/dlrm/static_quant/ipex/run_benchmark.sh: Language not supported

Comments suppressed due to low confidence (1)

examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/ipex/run_clm_no_trainer.py:201

• [nitpick] Consider renaming 'int8_bf16_mixed' to align with the new '--optimized' flag convention for consistency, if appropriate for this use case.

if args.optimized or args.int8_bf16_mixed: