fix: resolve execution blockers and hardcoded paths in llm_simple_qa benchmark

[Fahmid-Arman]

What type of PR is this?
/kind bug

What this PR does / why we need it:
The llm_simple_qa example previously contained multiple execution-blocking bugs, including hardcoded local environment paths, missing framework interfaces, and missing dataset automation. These issues prevented users from executing the benchmark out-of-the-box.

Changes implemented:

• Path Remediation: Replaced all hardcoded absolute paths (/home/icyfeather/...) and stale relative paths (./examples/llm/...) with accurate, portable root-relative paths in benchmarkingjob.yaml, testenv.yaml, and gen_algorithm.yaml.
• Dataset Automation: Added a prepare_data.py script to automatically generate the required JSONL data structure.
• Hardware Abstraction & Model Loading: Replaced the local model path with the Hugging Face Hub ID ("Qwen/Qwen2-0.5B-Instruct"). Replaced hardcoded cuda allocation with dynamic hardware detection ("cuda" if torch.cuda.is_available() else "cpu") to prevent immediate crashes on non-Nvidia/CPU-only machines.
• Framework Integration: Added the missing preprocess pass-through method to basemodel.py to satisfy the singletasklearning paradigm requirements.
• Documentation: Corrected the referenced Sedna zip file name (sedna-llm.zip) and updated the execution paths in README.md.

Which issue(s) this PR fixes:
Fixes #368

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[gemini-code-assist]

Code Review

This pull request updates the llm_simple_qa example by replacing hardcoded local paths with relative ones, adding a dataset preparation script (prepare_data.py), and updating basemodel.py to dynamically select the device and load the public Qwen/Qwen2-0.5B-Instruct model. The review feedback suggests enhancing device selection to support Apple Silicon (mps), explicitly setting torch.float32 on CPU to prevent half-precision runtime crashes, and adding a safety check in prepare_data.py to avoid a FileNotFoundError when creating directories.

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[Fahmid-Arman]

Feedback addressed.

• Added MPS support for Apple Silicon hardware.
• Enforced float32 precision for CPU execution to prevent Half backend errors.
• Added empty-string validation for directory creation in prepare_data.py.

/assign @jaypume