[LFX Term 1 2026] Restoring LLM Edge Benchmark Suite Single Task Bench

[NishantSinghhhhh]

What type of PR is this?
/kind bug
/kind feature

What this PR does / why we need it:
This PR restores and fixes the llm-edge-benchmark-suite (single_task_bench) so it successfully executes end-to-end within the current Ianvs SingleTaskLearning paradigm.

Previously, the LLM benchmarking pipeline was failing due to missing dependencies, pipeline contract mismatches (missing/strict method signatures), and inaccurate C++ stderr log parsing for latency metrics.

Key changes include:

• Added Dependencies: Created a requirements.txt to explicitly include llama-cpp-python and other necessary inference packages.
• Pipeline Contract Alignment: Added preprocess and postprocess methods to LlamaCppModel with optional arguments (data=None, **kwargs) to satisfy the strict signature checks of the Ianvs pipeline without throwing TypeError.

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[gemini-code-assist]

Code Review

This pull request introduces an LLM edge benchmark suite, adding a requirements file and implementing a base model class. The predict method was refactored to use streaming for measuring prefill latency, and dummy preprocess, postprocess, and train methods were added to satisfy pipeline requirements. A review comment suggests that the train method should return the model path from an instance variable rather than kwargs, as the latter may be empty during execution, leading to an incorrect return value.

[NishantSinghhhhh]

@MooreZheng sir, I have added Readme for LLM Edge Benchmark Suite Single Task Bench, a Requirements.txt and minimal changes in the Basemodel.py to make it work,

Screencast.from.2026-04-23.18-52-16.webm

[NishantSinghhhhh]

1. Optional pre-Process and Post process
2. Not changing the indentation
3. no unnecessary comments
4. Reasoning for prediction as 0

[MooreZheng]

1. try to make the preprocess optionable to save future burden
2. check whether there is problem for the prediction fuction. If not, just leave it there.