| --- |
| license: apache-2.0 |
| tags: |
| - coreml |
| - laya |
| - decision-model |
| --- |
| |
| # laya-priority-coreml (English, priority + scheduling, up to 4 choices) |
|
|
| Core ML conversion of `convaiinnovations/laya` (English checkpoint, revision |
| `55cf4c4ebb4ebe31b2550e8bdf3bd21b99753851`), generalized from a fixed |
| binary `choice` question to a fixed **2–4 option** `choice` graph. Used for |
| two purposes in the consuming app: deciding whether a to-do item should be |
| acted on now versus deferred, and picking a free processing day/time slot |
| among up to four candidates. This is not a general-purpose export of Laya — |
| only the masked-choice decision graph is included, and only `logits` |
| (pre-temperature) is returned. |
|
|
| Weights © Convai Innovations, Apache-2.0. Conversion graph and Core ML export |
| by this repository's owner. |
|
|
| ## Known limitation — do not treat as validated for general use |
|
|
| Zero-shot measurement on the original 48-item balanced English/Korean-translated |
| priority fixture (this checkpoint, binary priority question) scored 17/24 |
| (70.8%) on English and 15/24 (62.5%) on Korean text machine-translated to |
| English, both below an 80%-accuracy / 70%-recall gate. This artifact is |
| shipped anyway at the consuming application's explicit request, with |
| rule-based fallback preserved for degraded cases. Do not present its output |
| as high-confidence, and treat the added multi-option scheduling question the |
| same way — it has not been separately accuracy-gated, only numerically |
| parity-checked against the source model below. |
|
|
| ## Static IO contract |
|
|
| - Inputs (`int32`): `input_ids [1,512]`, `attention_mask [1,512]`, |
| `marker_pos [1,4]`, `marker_mask [1,4]`, `qtype [1]` |
| - Output (`float32`): `logits [1,4]` — raw, pre-temperature logits from the |
| source `DecisionModel.scorer` head. Questions with 2 or 3 active options |
| set `marker_pos=0, marker_mask=0` on the unused trailing slot(s); the model |
| forces those logits to `-10000`, so only active options can win argmax. |
| - Special token ids: `pad=50283 cls=50281 sep=50282 mask=50284` |
| - Sequence format follows upstream `laya.common.build_sequence` with |
| `max_len=512, head_max_len=192`; instruction/option text is supplied by the |
| caller (fixed priority question, or a dynamic day/slot question), not fixed |
| in the graph. |
|
|
| ## Parity evidence (from the exporter that produced this package) |
|
|
| - Source PyTorch vs traced PyTorch: max abs error `0.0` (exact) across the |
| fixture cases, including a synthetic 4-option scheduling case. |
| - Widening the graph from 2 to 4 marker slots does not change the active-slot |
| source logits: re-running the original 2-option English/Korean fixtures |
| through the 4-slot graph (inactive slots masked out) reproduced the exact |
| same active logits (`max_abs_error 0.0`). |
| - Native Core ML (`CPU_AND_NE` requested) vs source, 49 cases (24 English + |
| 24 real Korean→English machine translations + 1 synthetic 4-option |
| scheduling case): argmax agreement 49/49 in the recorded run; a repeated |
| run showed 48/49 because one English case (`en-18`) has a near-zero source |
| margin and is numerically borderline — this is disclosed, not hidden. Max |
| abs logit error over active options ≈0.23–0.36 depending on run; max abs |
| error over masked/inactive options is `0.0` in every run (they are pinned |
| to `-10000` and never move). |
| - An FP32 variant reproduces the source almost exactly but the compiler |
| prefers CPU for all ops in that variant, so it is not an ANE artifact; it |
| is not published here. |
|
|
| ## Files |
|
|
| - `laya_english.mlpackage/` — compiled-at-load Core ML package (FP16, |
| `logits`-only output, 4 marker slots) |
| - `tokenizer.json`, `tokenizer_config.json` — copied unmodified from the |
| source checkpoint's `tokenizer/` |
| - `laya_manifest.json` — file list with SHA-256 and sizes, IO contract, |
| special token ids, and masking semantics |
|
|
