onnx-genai-example-qwen2-5-0-5b-cuda-gqa-f16

Private real-weight ONNX package produced by Mobius from Qwen/Qwen2.5-0.5B-Instruct at immutable revision 7ae557604adf67be50417f59c2c2f167def9a775. Source license: apache-2.0.

This package contains 24 CUDA com.microsoft::GroupQueryAttention nodes. weight_integrity.json validates every packed QKV initializer against the pinned source weights.

Contents

• Canonical, hashless inference_metadata.yaml
• ONNX graphs and external-data weights
• Complete tokenizer/processor assets
• request.json and output.json real runtime evidence
• graph_report.json, performance.json, source.json, and provenance.json

Observed output: Write one short sentence about ONNX. ONNX is a popular open-source framework

Exact download

hf download justinchuby/onnx-genai-example-qwen2-5-0-5b-cuda-gqa-f16 --repo-type model --local-dir ./qwen2.5-0.5b-cuda-gqa-f16

ONNX Runtime load smoke test

python - <<'PY'
from pathlib import Path
import onnxruntime as ort

root = Path("qwen2.5-0.5b-cuda-gqa-f16")
for relative_path in ['model.onnx']:
    session = ort.InferenceSession(
        str(root / relative_path),
        providers=['CUDAExecutionProvider', 'CPUExecutionProvider'],
    )
    print(relative_path, session.get_providers(), [x.name for x in session.get_inputs()])
PY

The exact successful probe request, output, versions, providers, and timings are preserved in request.json, output.json, and performance.json.

Annotated inference metadata

Review inference_metadata.annotated.yaml for inline explanations of this package's workflow, tensor/state/cache contracts, and fail-closed omissions. inference_metadata.yaml remains the canonical machine-authored contract; automated validation confirms both files parse to the same metadata object.