Maya v0.1

.gitattributes

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LICENSE

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NOTICE.md

@@ -0,0 +1,30 @@
+# Notice
+
+Maya is an unofficial experiment. It is not affiliated with or endorsed by ConvAI Innovations, Answer.AI, LightOn or the authors of the NLI models it is compared against.
+
+## This project
+
+The scripts, evaluation code and results in this repository are Copyright 2026 vishalmysore and licensed under the Apache License, Version 2.0 (see `LICENSE`). The evaluation cases in `data/eval` are copied from the author's layaForWeb repository (same license).
+
+`results/laya_moe_eval_reference.json` holds the laya-typed-decisions and layaMOE answers recorded by the author's layaMOE repository (https://github.com/vishalmysore/layaMOE).
+
+## Maya's weights
+
+Maya is fine-tuned from MoritzLaurer/ModernBERT-base-zeroshot-v2.0 (Apache-2.0, https://huggingface.co/MoritzLaurer/ModernBERT-base-zeroshot-v2.0), itself fine-tuned from answerdotai/ModernBERT-base (Apache-2.0). Changes: full fine-tune on Maya's synthetic yes/no data plus MNLI, labels renamed to yes/no. The released weights are a modified derivative.
+
+## Training data
+
+- `gen/laya_domains.py` is copied from the author's layaMOE repository (same license).
+- During training, a few thousand MNLI pairs (GLUE `mnli`, https://huggingface.co/datasets/nyu-mll/glue) are mixed in. They are downloaded at run time and not redistributed.
+
+## Models used as baselines
+
+Downloaded from Hugging Face at run time, not redistributed here. Check each model card for its license and training data before reusing a model.
+
+- convaiinnovations/laya-typed-decisions (Apache-2.0)
+- cross-encoder/nli-deberta-v3-xsmall
+- MoritzLaurer/deberta-v3-xsmall-zeroshot-v1.1-all-33
+- MoritzLaurer/deberta-v3-base-zeroshot-v2.0
+- MoritzLaurer/ModernBERT-base-zeroshot-v2.0
+- MoritzLaurer/deberta-v3-large-zeroshot-v2.0
+checkpoints/ (model weights) are not in git; they go to https://huggingface.co/VishalMysore/maya.

README.md

@@ -1,3 +1,17 @@
 ---
 license: apache-2.0
+base_model: VishalMysore/maya
+tags: [onnx, onnxruntime-web, browser, wasm, yes-no, guardrail, quantized]
+language: [en]
 ---
+
+# Maya for the browser (int8 ONNX)
+
+[Maya](https://huggingface.co/VishalMysore/maya), a 150M-parameter yes/no gate, exported for ONNX Runtime Web: one graph, int8 weight-only quantization (MatMulNBits block 128, int8 embeddings), 161 MB.
+
+- `model.onnx` + `model.onnx.data` split into 24 MiB parts (`model.onnx.data.partNNN`); `manifest.json` lists the parts with the total size and SHA-256. Concatenate the parts in order to get `model.onnx.data`.
+- Inputs `input_ids`, `attention_mask` (int64, [batch, seq]) from `tokenizer.json`, encoding the pair `(text, statement)`; output `logits` [batch, 2].
+- P(yes) = sigmoid((logits[:, 0] - logits[:, 1]) / temperature), temperature in `maya_config.json` (2.8).
+- Checked against PyTorch on 256 hand-labeled answers: 81.6% accuracy (PyTorch 81.25%), 1 answer flips, largest probability change 0.12.
+
+Code and evaluation: https://github.com/vishalmysore/maya. Same weak spots as the PyTorch model (agent guardrails, minimal pairs in unseen domains); do not use it as the only safety check.

manifest.json

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+{
+  "version": 1,
+  "model": {
+    "onnx": "model.onnx",
+    "data": {
+      "name": "model.onnx.data",
+      "size": 155134720,
+      "sha256": "36748a400ff30da7f1088d9917c94e4f4a4eec06342373a647f01ab07696f98a",
+      "parts": [
+        "model.onnx.data.part000",
+        "model.onnx.data.part001",
+        "model.onnx.data.part002",
+        "model.onnx.data.part003",
+        "model.onnx.data.part004",
+        "model.onnx.data.part005",
+        "model.onnx.data.part006"
+      ]
+    }
+  },
+  "inputs": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "output": "logits",
+  "p_yes": "sigmoid(logits[:, 0] - logits[:, 1])",
+  "pair_order": [
+    "text",
+    "statement"
+  ],
+  "verification": {
+    "max_abs_dp": 0.11761707067489624,
+    "flips_at_0.5": 1,
+    "acc_pytorch": 0.8125,
+    "acc_int8": 0.81640625,
+    "n": 256
+  }
+}

maya_config.json

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+{
+ "p_yes": "sigmoid((logit_yes - logit_no) / temperature)",
+ "temperature": 2.8,
+ "temperature_fitted_on": "data/eval (v1, 120 hand-labeled answers); lowered v2 ECE from 0.152 to 0.061",
+ "thresholds": {
+  "t_yes": 0.5,
+  "t_no": 0.5
+ },
+ "thresholds_note": "Default is strict yes/no. For 'not sure' with a bounded error rate, fit maya.conformal.Abstainer on a labeled sample of your own inputs (a few hundred answers per side are needed to certify ~10% error)."
+}

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "[UNK]"
+}