model-checker

name	model-checker
description	Validate a newly supported optimum-intel model with OpenVINO GenAI. Use when: checking new model support, verifying model export to OpenVINO IR, running GenAI inference test with llm_bench, benchmarking model accuracy with who-what-benchmark.
argument-hint	model_id and task (e.g. tencent/HY-MT1.5-1.8B text-generation-with-past)

Model Checker

Validates that a HuggingFace model exported via optimum-intel works correctly with OpenVINO GenAI pipelines and passes accuracy benchmarks.

When to Use

• A new model was added to optimum-intel and needs GenAI validation
• Verify a HuggingFace model exports to OpenVINO IR and runs inference
• Check model accuracy after conversion using who-what-benchmark

Inputs

The user must provide:

• model_id: HuggingFace model identifier (e.g. tencent/HY-MT1.5-1.8B) or path to an existing OpenVINO IR directory (e.g. /tmp/my_model_ir). When a local path is provided, export is skipped automatically.
• task: optimum-cli export task. Supported values:
  • text-generation-with-past
  • image-text-to-text
  • text-to-image
  • image-to-image
  • feature-extraction
  • text-classification
  • text-to-video
  • automatic-speech-recognition

Prerequisites

Ensure the Python virtual environment is activated before running any commands.

1. Locate the virtual environment — check for common directories at the repository root: .venv/, venv/, env/. Use list_dir to find it. If none is found, ask the user for its location.
2. Check if already activated: if which python or where python points inside the virtual environment, it's already activated. If not, proceed to activate it.
3. Activate based on the current platform:
   • Linux/macOS: source <venv_path>/bin/activate
   • Windows (cmd): <venv_path>\Scripts\activate.bat
   • Windows (PowerShell): <venv_path>\Scripts\Activate.ps1
4. The background terminal doesn't inherit the venv activation. Run it with the venv activated in the same command.

Procedure

Step 1: Run check_model.py

Run the checker script from the repository root:

python3 .github/skills/model-checker/scripts/check_model.py \
    --model-id <model_id_or_path> \
    --task <export_task> \
    --work-dir .model_enabler/model_checker

When --model-id is a path to an existing directory, the script treats it as a pre-converted OpenVINO IR model and automatically skips the export step.

Run python3 .github/skills/model-checker/scripts/check_model.py --help for the full argument reference including defaults. The --work-dir is where all intermediate files, logs, and outputs will be stored. Do not pipe with any additional logging or redirection — the script handles its own logging.

Skip flags (for re-runs after a fix)

When a previous run already passed some steps (e.g. export succeeded but inference test failed), use skip flags to avoid repeating expensive passed steps:

• --skip-export — reuse existing IR in <work-dir>/model_ir instead of re-exporting (avoids re-downloading weights). When --model-id is a local path, export is bypassed automatically and that directory is used directly instead of <work-dir>/model_ir.
• --skip-llm-bench — skip the llm_bench inference test
• --skip-wwb — skip the who-what-benchmark accuracy check
• --skip-wwb-ground-truth — skip WWB ground-truth collection and reuse <work-dir>/wwb/gt.csv; useful when iterating on GenAI target evaluation after ground truth was already collected

Do not use skip flags on the first run. Only use them when retrying after a targeted fix.

Step 2: Interpret Results

The script logs progress for each step and exits with code 0 (pass) or non-zero (fail).

Pass criteria:

• Export: exit code 0

• Inference test (llm_bench): exit code 0, metrics line logged

• WWB accuracy (depends on --wwb-base mode):

  • --wwb-base optimum (default):
    1. Optimum ground truth generation: exit code 0
    2. GenAI target evaluation: similarity ≥ SIMILARITY_THRESHOLD
  • --wwb-base hf:
    1. HF ground truth generation: exit code 0
    2. Optimum target evaluation: similarity ≥ SIMILARITY_THRESHOLD
    3. GenAI target evaluation: similarity ≥ SIMILARITY_THRESHOLD

  Note: the WWB step is skipped automatically for automatic-speech-recognition (no WWB support).

Log files: each tool writes its own dedicated log; paths are printed during execution. When a step fails, read the corresponding log for the full traceback and context before drawing any conclusions.

work-dir: work-dir is in current workspace, prefer to use tool calls to access logs and outputs instead of custom bash commands.

Step 3: Report Results

Results format:

• Model: <model_id> (<task>)
• Validation: PASSED / FAILED
• Performance (if passed):
  • 1st token latency, 2nd token latency, throughput
  • Optimum similarity / GenAI similarity (if applicable)
• Logs: paths to export log, llm_bench log, WWB logs
• Failed step analysis (if failed): summary of the failure and relevant log path for details

Security

• NEVER invoke optimum-cli, wwb, or llm_bench directly. Always go through check_model.py.
• NEVER modify model_id — pass it exactly as provided by the user.