ios/: the JIT .aimodel (every iPhone generation specializes it); the h19p AOT files move to ios-h19p/

The h19p AOT package loads only on the device architecture it was compiled for; the JIT .aimodel specializes on every iPhone generation (measured 2026-09-26 on the iPhone 18 Pro). ios/ now carries the same JIT files as macos/; the h19p package stays at ios-h19p/ with the other files of ios/. Record: knowledge/jit-distribution.md in john-rocky/coreai-model-zoo.

.gitattributes

@@ -37,3 +37,7 @@ ios/gliner25-decide_float16_s256_m32.h19p.aimodelc/main-h19p-delegates/MPSGraph/
 ios/gliner25-decide_float16_s512_m32.h19p.aimodelc/main-h19p-delegates/MPSGraph/mpsExecutable.mpsgraphpackage/specialized_model_0.mpsgraph filter=lfs diff=lfs merge=lfs -text
 macos/gliner25-decide_float16_s256_m32.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text
 macos/gliner25-decide_float16_s512_m32.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text
+ios-h19p/gliner25-decide_float16_s256_m32.h19p.aimodelc/main-h19p-delegates/MPSGraph/mpsExecutable.mpsgraphpackage/specialized_model_0.mpsgraph filter=lfs diff=lfs merge=lfs -text
+ios-h19p/gliner25-decide_float16_s512_m32.h19p.aimodelc/main-h19p-delegates/MPSGraph/mpsExecutable.mpsgraphpackage/specialized_model_0.mpsgraph filter=lfs diff=lfs merge=lfs -text
+ios/gliner25-decide_float16_s256_m32.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text
+ios/gliner25-decide_float16_s512_m32.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text

ios-h19p/classifier.json

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+{
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+  "revision": "7ee5da4c2415e32259bcdc0b1a7367c32ce8d6f6",
+  "dtype": "float16",
+  "MMAX": 32,
+  "shapes": [
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+      "bundle": "gliner25-decide_float16_s256_m32.h19p.aimodelc",
+      "bytes": 974005777,
+      "reference": "reference_s256.json"
+    },
+    {
+      "S": 512,
+      "bundle": "gliner25-decide_float16_s512_m32.h19p.aimodelc",
+      "bytes": 975579352,
+      "reference": "reference_s512.json"
+    }
+  ],
+  "arch": "h19p",
+  "device": "iPhone 18 Pro (iPhone19,2)",
+  "host_shape_rule": "use the smallest S in `shapes` with len(input_ids) <= S; longer inputs are outside these bundles (gliner2's classify_text_long chunks such text on the host)",
+  "graph": {
+    "relative_position": "bucket table baked at export (S fixed); coreai-torch 0.4.1 lowers aten.div.Tensor(int,int) to integer division — logs/diag_div_probe.log"
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+    "[R]": 128006,
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+    "attention_mask": "1 for real tokens, 0 for padding",
+    "label_idx": "unused slots repeat the first [L] position (host never reads them)"
+  },
+  "label_idx": "the [L] marker positions of every task, concatenated in task order; the host keeps each task's slice",
+  "layout": "( [P] <task>[: <prompt>][ [DESCRIPTION] <label>: <desc>]* ( [L] <label> [L] <label> ... ) ) [SEP_STRUCT] ( ... ) [SEP_TEXT] <text words>; every piece tokenized with tokenizer.tokenize(piece) on its own, no CLS/SEP",
+  "text": "append '.' unless the text ends with . ! ? (empty -> '.'); split with gliner2 WhitespaceTokenSplitter regex (URL | email | @handle | \\w+(?:[-_]\\w+)* | \\S, IGNORECASE); lowercase the word values only; schema strings keep their case",
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+    "multi_label": "sigmoid, every label with prob >= cls_threshold; none -> argmax",
+    "cls_threshold_default": 0.5,
+    "temperature": 1.0
+  }
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ios-h19p/reference_s256.json

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ios-h19p/reference_s512.json

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+ "revision": "7ee5da4c2415e32259bcdc0b1a7367c32ce8d6f6",
+ "S": 512,
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+ "selection": "longest-distance range first (S/2 < seq_len <= S), then multi-label, most tasks, longest",
+ "seq_len": 304,
+ "text": "[place] Cactus Relay, Arroyo Bend\n[text] Why did we wait so long to try it? My date and I had booked a table at Cactus Relay for our first dinner together, arriving during a neighborhood festival whose live brass band was playing just beyond the block. I was new to Arroyo Bend, so finding the entrance took a minute, and finding a clean empty table took longer; one had sticky rings, another held abandoned glasses, but a host quickly reset a small table near the window. The room was bright, crowded, and cheerful, with papel picado overhead, though conversation competed with the music outside. We ordered tacos al pastor, charred mushroom tacos, and two aguas frescas. Everything tasted fresh, especially the pineapple salsa and the warm tortillas, although the mushroom filling needed more seasoning. Service was courteous and accurate, if somewhat stretched by the event crowd. Compared with fictional neighbors Luna Comal and Barrio Azul, Cactus Relay felt less polished but more generous with portions; prices were reasonable for a full-service place. The wait for our food reached nearly thirty-five minutes, yet our server explained the delay and brought salsa macha. Clean cutlery and a spotless restroom reassured me, but the neglected first tables left a mixed impression. I would return for tacos, perhaps para llevar, after the festival rush.",
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+}

ios-h19p/tokenizer/special_tokens_map.json

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+{
+  "additional_special_tokens": [
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+    {
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+      "single_word": false
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+    {
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+    {
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+    {
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+    {
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+      "lstrip": false,
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+      "single_word": false
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+    {
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+    },
+    {
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+  "bos_token": {
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+    "lstrip": false,
+    "normalized": false,
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+  "mask_token": {
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+  "sep_token": {
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+  "unk_token": {
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+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

ios-h19p/tokenizer/tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
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+      "lstrip": false,
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+    "[L]",
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+    "[OUTPUT]",
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+  "backend": "tokenizers",
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
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-      "bundle": "gliner25-decide_float16_s256_m32.h19p.aimodelc",
-      "bytes": 974005777,
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       "reference": "reference_s256.json"
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-      "bytes": 975579352,
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-  "device": "iPhone 18 Pro (iPhone19,2)",
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