| --- |
| tags: |
| - research |
| - custom-code |
| - program-synthesis |
| --- |
| # ICL-Native R5: bounded semantic opcode kernel |
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| Private custom-Python model staging. Actual 2,774 trained parameters, no base model. This model selects finite opcodes/errors from structured input; it is NOT general source-text generation, an LLM or a learned arithmetic system. |
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| ```sh |
| python -I -S inference.py --request examples/demo.json |
| python -I -S validate.py |
| ``` |
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| Only Python 3.11+ standard library is needed. `model/weights.json` holds the selected seed17 fitted weights. The model graph and all twelve core runtime files are pinned by the original manifest. `examples/demo.json` returns Ok(-4) after independent VM verification. Context/memory errors and denied journal examples are included. No external tools are installed. Confidence is uncalibrated. |
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| Read SOURCE_MODEL_CARD.md for the original 18-run training record, 16 intents, 22 classes, 1–32 supplied steps, split design and negative budget-comparison result. Historical benchmark scores are not new replications here. Exact arithmetic, code emission, state, memory and permissions are deterministic. The 30,000-example data and complete checkpoints are preserved in the sibling `github/icl-native-r5` research package. |
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| This repository is not Transformers AutoModel/Inference API compatible. Public licensing, rights and author approval are pending; no public HF upload is performed. Source code remains unmodified in this repackaging. No license is granted by this preparation. |
| ## Verified packaging revision — 2026-10-20 |
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| Preserved model weights reloaded; saved or synthetic example checks passed. No training, held-out evaluation, clinical or hosted verification. |
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| Runtime state: `PASS`. Rights and release approval are separate. See `RELEASE_VERIFICATION.json`. No paper or conference submission is included as a new software result. |
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