Release validated Core ML bundle and model card

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,176 @@
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+      the terms of any separate license agreement you may have executed
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+      whether in tort (including negligence), contract, or otherwise,
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+   END OF TERMS AND CONDITIONS

NOTICE

@@ -0,0 +1,22 @@
+laya-coreml
+Copyright 2026 laya-coreml contributors
+
+This product includes software adapted from Laya (Apache-2.0):
+https://github.com/NandhaKishorM/laya
+Copyright Convai Innovations and Laya contributors.
+Upstream source revision: 6a5819129eb220570792e417e49723d697efd76f
+
+Prompt construction, tokenization, result formatting, validation fixtures,
+the ModernBERT architecture implementation, and the Snake game, policy, terminal
+UI, recording/export and benchmark tools are adapted from laya-mlx:
+https://github.com/mizorewww/laya-mlx
+Copyright 2026 laya-mlx contributors. Licensed under Apache-2.0.
+Adapted revision: fc1df62828a3fedf4d8229fdac1cbd85f1cdf337
+Changes: Core ML runtime, export-only PyTorch graph, fixed/ranged shape input
+handling, Core ML validation, device planning, and performance measurement;
+ANE graph/runtime, portable model bundles and offline Core ML Snake integration.
+
+The model follows Laya's DecisionModel and Hugging Face Transformers ModernBERT.
+Original checkpoint weights belong to their respective upstream authors and
+are downloaded separately. No weights are included in this repository.
+This is an independent project, not an official Convai Innovations or Apple release.

README.md

@@ -0,0 +1,90 @@
+---
+license: apache-2.0
+library_name: coreml
+pipeline_tag: text-classification
+base_model: convaiinnovations/laya-multilingual
+tags:
+- coreml
+- laya
+- apple-silicon
+- decision-model
+- local-ai
+- modernbert
+---
+
+# laya-multilingual-coreml-snake
+
+**Laya typed decisions on Apple Silicon, using CPU + GPU.**
+This is a portable Core ML bundle for [laya-coreml](https://github.com/mizorewww/laya-coreml),
+converted from [convaiinnovations/laya-multilingual](https://huggingface.co/convaiinnovations/laya-multilingual).
+It outputs `choice`, `score`, and `noul` probabilities with **zero generated tokens**.
+Inference needs no PyTorch, Transformers, MLX, remote code, or cloud API.
+
+## Run
+
+Apple Silicon, macOS 15+, Python 3.11–3.13. Tested on M3 Max / macOS 27.2.
+
+```bash
+pip install laya-coreml
+```
+
+```python
+import laya_coreml as laya
+
+agent = laya.load("aac6fef/laya-multilingual-coreml-snake")  # Download once; Core ML runs locally.
+result = agent.predict(
+    "The customer asks for a refund of a duplicate payment.",
+    {"refund": {"type": "noul", "instructions": "Does the customer request a refund?"}},
+)
+print(result["answers"])
+```
+
+To download explicitly and then run entirely offline:
+
+```bash
+hf download aac6fef/laya-multilingual-coreml-snake --local-dir models/snake
+pip install 'laya-coreml[demo]'
+laya-coreml-snake --model models/snake --fps 12
+```
+
+Use `laya.load("aac6fef/laya-multilingual-coreml-snake", local_files_only=True)` for a cached snapshot or pass a
+local directory. Use `revision="<Hub commit SHA>"` to pin a remote revision.
+
+## Format and fidelity
+
+This FP16 export retains the original model architecture and decision schema. The enumerated-length GPU export is the validated general-purpose configuration.
+
+The matching fixed B3/L64 Snake export agreed with MLX on 600/600 actions across two 300-step trajectories, with zero deaths and zero safety interventions. It is a specialized 64-token export, not the full-context general-purpose model.
+
+The exported capacity is **64 total tokens**, batch **3**,
+and **4** option slots. Questions/options and state share this budget.
+The ANE short exports reject over-capacity prompts. Snake uses planner features and a
+visible optional cycle safety shield; survival is not a claim of unaided game intelligence.
+
+`coreml_config.json` records shapes, source revisions and per-file SHA256 checksums.
+`validation.json` contains the packaging-time validation. Port fidelity on this regression
+suite does not establish general task accuracy or preserved calibration on arbitrary inputs.
+
+## Performance and limits
+
+The multilingual **ANE L96 FP16** runtime measured **4.98 / 5.31 ms P50 / P95** for
+one short question on M3 Max; W8 measured **4.88 / 5.23 ms**. Whole-system energy per
+decision improved **2.78× / 3.19×**, respectively, against compiled MLX FP16 in that
+experiment. Those numbers apply to the named short ANE variants, not every bundle,
+long contexts, or complete Snake frames. The requested 10× improvement was not achieved.
+
+[Measurements and scope](https://github.com/mizorewww/laya-coreml/blob/main/docs/ANE_BENCHMARKS.md)
+· [General Core ML benchmarks](https://github.com/mizorewww/laya-coreml/blob/main/BENCHMARKS.md)
+· [Snake demo](https://github.com/mizorewww/laya-coreml/blob/main/docs/SNAKE_DEMO.md).
+
+## Provenance
+
+- Original checkpoint: `convaiinnovations/laya-multilingual` at `052592a15d198d9ad47da779604259b10b47b7aa`.
+- Original weights SHA256: `9d628fd971b700382ac6f65920a86f149777b2e748e0c955fb3b19695aa8f204`.
+- Upstream implementation: [NandhaKishorM/laya](https://github.com/NandhaKishorM/laya),
+  commit `6a5819129eb220570792e417e49723d697efd76f`.
+- Original models and code are by Convai Innovations and contributors, Apache-2.0.
+- Independent conversion; not an official Convai Innovations or Apple release.
+
+See `LICENSE` and `NOTICE`. Model quality and task/language limitations originate
+with Laya; this runtime is an inference port, not a newly trained decision model.

coreml_config.json

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+{
+  "format": "laya-coreml",
+  "format_version": 1,
+  "source": "convaiinnovations/laya-multilingual",
+  "revision": "052592a15d198d9ad47da779604259b10b47b7aa",
+  "source_weights_sha256": "9d628fd971b700382ac6f65920a86f149777b2e748e0c955fb3b19695aa8f204",
+  "precision": "float16",
+  "attention": "sdpa",
+  "attention_mask_construction": "integer_positions_v2",
+  "minimum_deployment_target": "macOS15 / iOS18",
+  "shape": {
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+    "min_length": 16,
+    "default_length": 64,
+    "max_options": 4,
+    "flexible": false,
+    "mode": "fixed",
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+  },
+  "versions": {
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+    "torch": "2.7.0",
+    "numpy": "2.1.3"
+  },
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+}

encoder/config.json

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+    "ModernBertForMaskedLM"
+  ],
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+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 0,
+  "position_embedding_type": "sans_pos",
+  "repad_logits_with_grad": false,
+  "rope_parameters": {
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+      "rope_type": "default"
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+      "rope_type": "default"
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+  "sparse_prediction": false,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "vocab_size": 256000
+}

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model.mlpackage/Manifest.json

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rl_agent_config.json

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tokenizer/tokenizer_config.json

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+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<bos>",
+  "eos_token": "<eos>",
+  "extra_special_tokens": {
+    "extra_0": "<start_of_turn>",
+    "extra_1": "<end_of_turn>"
+  },
+  "mask_token": "<mask>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
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+  "pad_token": "<pad>",
+  "padding_side": "right",
+  "sep_token": "<eos>",
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "<unk>"
+}

validation.json

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+{
+  "environment": {
+    "created_at": "2026-09-20T03:20:11.091953+00:00",
+    "chip": "Apple M3 Max",
+    "memory_bytes": 137438953472,
+    "os": "macOS-27.2-arm64-arm-64bit",
+    "python": "3.12.13",
+    "packages": {
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+      "numpy": "2.1.3",
+      "torch": "2.7.0",
+      "tokenizers": "0.23.2",
+      "mlx": "0.32.2",
+      "laya-mlx": "0.1.0"
+    },
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+  "format": "laya-coreml",
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+}