ara-diac-layerdrop-1.0
ara-diac-layerdrop-1.0-int4
IMF v1 (int4, decoder kv, opset 14). Trained from the depth-cut rung: ByT5-small with the encoder halved 12->6 (surviving layers copied VERBATIM from pretraining - both width-cut approaches collapsed at 74.68/82.96 while this works), Muon optimizer, r7 teacher labels (the 2.0 recipe), 6 epochs. ~190M parameters (63% of ByT5-small). Full-set windowed DER-CE 5.784, delta CI [3.033, 3.491] vs in-run teacher 2.2921 — the depth premium over the full-depth G2a peer (4.5701, CI [1.911, 2.352]) is 1.21pp with non-overlapping intervals. The int4 variant (~95MB) is the browser-budget tier; margin-gated before ship. Checkpoint rababa-checkpoints:/rababa_arabic_distill_small/run-009-layerdrop-6ep/best..
| field | value |
|---|---|
| task | diacritization (Arab → Arab) |
| artifact | ara-diac-layerdrop-1.0-int4.zip (0.14 GiB) |
| der_teacher_fullset | 2.2921 — windowed DER-CE (1400-byte windows, word-boundary split, greedy, haraqat-projected, Misraj evaluator); full 1,200-paragraph SadeedDiac-25 |
| der_student_fullset | 5.784 — same harness; paired bootstrap delta 3.2455 [3.033, 3.491] |
| parity | cer_delta 0.0589pp on 2480 samples |
| sha256 | 352e02105645a6e283e2c3602505bea0e90085b741666fd863e478e4835e7bc9 |
| license | BSD-3-Clause |
Runtimes reassemble split parts transparently and verify every sha256:
from secryst import Model
model = Model.load("ara-diac-layerdrop-1.0-int4")