Instructions to use SZLHOLDINGS/szl-ouroboros with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use SZLHOLDINGS/szl-ouroboros with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("SZLHOLDINGS/szl-ouroboros") - Notebooks
- Google Colab
- Kaggle
forge: ship REAL trained surrogate v1 for szl-ouroboros (model.joblib + receipt + scripts; honest card + provenance)
5fa1476 verified | { | |
| "schema": "szl.model-source-attestation/v1", | |
| "model": { | |
| "id": "SZLHOLDINGS/szl-ouroboros", | |
| "repository_type": "model", | |
| "artifact_kind": "kernel-code-and-configuration + trained surrogate (model.joblib)", | |
| "trained_weights_present": true, | |
| "stdlib_only": false, | |
| "torch_required": false, | |
| "surrogate": { | |
| "file": "model.joblib", | |
| "sha256": "6c4067810eb508dfb61f3cfaf84b6db81cf2f7c78286f02e786205e0b014ac65", | |
| "role": "loop-tax regressor (predicts kernel-derived overheadMs) \u2014 kernel remains sole ground truth", | |
| "fidelity_MEASURED": { | |
| "held_out_MAE_ms": 15.5853, | |
| "held_out_R2": 0.9822, | |
| "held_out_target_std_ms": 191.079 | |
| }, | |
| "receipt": "TRAINING_RECEIPT.json", | |
| "reverify": "python scripts/eval.py" | |
| }, | |
| "stdlib_only_note": "kernel package itself stays stdlib-only; the OPTIONAL surrogate needs sklearn+joblib" | |
| }, | |
| "source_of_record": { | |
| "state": "VERIFIED_HF_SOURCE_OF_RECORD", | |
| "platform": "huggingface", | |
| "repository": "SZLHOLDINGS/szl-ouroboros" | |
| }, | |
| "mirrors": { | |
| "live_surface": "a11oy backbone loop tax (artifacts/api-server/src/lib/backbone.ts buildLoopTrace/sumAttemptMs/peakAttemptMs/deadHopMs)", | |
| "timing_basis": "LOOP_TIMING_BASIS carried verbatim from backbone.ts", | |
| "lean_repo": "szl-holdings/lutar-lean", | |
| "doi_lutar_lean": "10.5281/zenodo.20434308" | |
| }, | |
| "verification": { | |
| "tests": "13/13 passed", | |
| "tests_basis": "MEASURED local python3 -m pytest run, stdlib-only, offline", | |
| "falsifiability": "arithmetic is falsifiable (a wrong split flips the asserts); an unmeasured wall yields overheadMs=None (UNAVAILABLE, not fabricated); a missing latency raises; a budget violation is surfaced" | |
| }, | |
| "lambda": { | |
| "status": "Conjecture 1 (open) \u2014 uniqueness unproven", | |
| "touched_by_this_artifact": false | |
| }, | |
| "claims": { | |
| "trained_model": "CLAIMED \u2014 sklearn HistGradientBoostingRegressor, MEASURED, receipted", | |
| "reproducible_build": "NOT_CLAIMED", | |
| "weights_present": "model.joblib (sklearn surrogate; kernel stays weightless)" | |
| }, | |
| "limits": [ | |
| "The trained surrogate APPROXIMATES the kernel's DERIVED overheadMs from trace shape; it does NOT replace the kernel's exact arithmetic, which remains authoritative.", | |
| "Runs with an unmeasured wall (overheadMs=None / UNAVAILABLE) are dropped from training \u2014 never fabricated.", | |
| "modelMs and peakAttemptMs are MEASURED (upstream wall windows); overheadMs, serializationTaxMs and deadHopMs are DERIVED arithmetic on those measurements \u2014 never new claims.", | |
| "serializationTaxMs is a COUNTERFACTUAL (what a perfectly-parallel loop could save), never a realized saving \u2014 Alloy's loop is strictly sequential.", | |
| "deadHopMs is an upper bound only \u2014 Alloy does not prefetch.", | |
| "overheadMs is UNAVAILABLE (None) when the run wall was not measured \u2014 never fabricated.", | |
| "No .bin, .safetensors, .pt, .pth, .onnx, or .gguf weight artifact is present.", | |
| "Loop-tax accounting does not prove or upgrade \u039b, which remains Conjecture 1 (open)." | |
| ] | |
| } |