Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,6 @@ 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
+assets/tron-vs-jev-evals.png filter=lfs diff=lfs merge=lfs -text
+assets/tron-vs-jev-index.png filter=lfs diff=lfs merge=lfs -text
+assets/tron-vs-jev-table.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,18 @@
+Tron-1B model weights
+Copyright (c) 2026 Samir Sengupta
+
+Licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
+
+You are free to share (copy and redistribute in any medium or format) and adapt (remix, transform, and build upon)
+this material, under the following terms:
+
+  Attribution    You must give appropriate credit, provide a link to the license, and indicate if changes were made.
+  NonCommercial  You may not use the material for commercial purposes.
+
+No additional restrictions: you may not apply legal terms or technological measures that legally restrict others
+from doing anything the license permits.
+
+Full legal code: https://creativecommons.org/licenses/by-nc/4.0/legalcode
+
+This license covers the model weights and configuration in this repository. The troncore runtime used to run them
+is licensed separately under Apache-2.0. Third-party components are listed in NOTICE.

NOTICE

@@ -0,0 +1,23 @@
+Tron-1B
+Copyright (c) 2026 Samir Sengupta
+
+This model was initialised from the Ettin encoder:
+
+  jhu-clsp/ettin-encoder-1b  (https://huggingface.co/jhu-clsp/ettin-encoder-1b)
+  Weller, Ricci, Marone, Chaffin, Lawrie, Van Durme. "Seq vs Seq: An Open Suite of Paired Encoders and Decoders",
+  arXiv:2507.11412, 2025.
+  Licensed under the MIT License:
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+    documentation files (the "Software"), to deal in the Software without restriction, including without limitation
+    the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and
+    to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+    the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
+    THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+    CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS IN THE SOFTWARE.

README.md

@@ -0,0 +1,127 @@
+---
+license: cc-by-nc-4.0
+language:
+- en
+library_name: troncore
+base_model: jhu-clsp/ettin-encoder-1b
+pipeline_tag: zero-shot-classification
+tags:
+- decision-model
+- zero-shot-classification
+- text-classification
+- intent-classification
+- prompt-injection
+- calibration
+- modernbert
+---
+
+# Tron-1B
+
+**Tron-1B answers typed questions about text or JSON in a single pass: choose one option, rate on a scale, or answer yes/no, with calibrated probabilities.** It is built for the fast decisions around an AI application: routing, triage, safety screening, intent detection, and workflow automation.
+
+- **Accurate:** beats Jev 1.13.0 on all four of its published benchmarks (below).
+- **Fast:** 16.7 ms per decision (p50) on one GPU.
+- **Calibrated:** its confidence scores track how often it is right, so you can gate actions on them.
+- **Any question, at request time:** you write the question and the options; no retraining for new label sets.
+
+![Decision Index](assets/tron-vs-jev-index.png)
+
+## Quickstart
+
+```bash
+pip install troncore
+```
+
+```python
+from troncore import Engine
+
+eng = Engine("SamCodeManMk2/tron-1b")          # downloads once, then runs locally (GPU if available)
+
+answers = eng.decide(
+    {"subject": "Duplicate charge on invoice #4411",
+     "body": "We were billed twice for March. Refund the duplicate today or we cancel our plan."},
+    {
+        "department": {"type": "choice", "instructions": "Which team should handle this?",
+                       "criteria": {"billing": "invoices, payments, refunds", "technical": "bugs, outages",
+                                    "sales": "pricing, new contracts", "other": "anything else"}},
+        "urgency":    {"type": "score", "instructions": "How urgent is this?",
+                       "criteria": ["not urgent", "this week", "today", "critical"]},
+        "churn_risk": {"type": "yesno", "instructions": "Does the customer threaten to cancel?"},
+    },
+)
+answers["department"]["choice"]        # 'billing'
+answers["churn_risk"]["probability"]   # P(yes)
+answers["department"]["confidence"]    # calibrated confidence of the top option
+```
+
+Every answer includes `probabilities`, `confidence`, `margin` (top minus second) and `entropy`. Pass
+`min_confidence=0.8` to get `abstain: true` on uncertain answers, so they can be routed to a person or a larger model.
+Questions with more than 128 options are handled automatically by scoring them in rounds. Long inputs can be read
+in sliding windows with `windows="all"`.
+
+### Question types
+
+| type | options | answer |
+|---|---|---|
+| `choice` | `criteria`: list of labels, or dict label → description | `choice` + a probability per label |
+| `score` | `criteria`: ordered list of levels | `score` (expected level), `level`, a probability per level |
+| `yesno` (alias `noul`) | fixed no / yes | `answer` (bool) + `probability` of yes |
+
+## Results
+
+![Evaluations](assets/tron-vs-jev-evals.png)
+
+| Benchmark | Tron-1B | Jev 1.13.0 |
+|---|---|---|
+| Banking77 (intent, 77 labels)¹ | **94.0** | 87.0 |
+| AG News (topic) | **93.9** | 91.0 |
+| typed-decisions (2,000 business decisions) | **79.6** | 72.7 |
+| DAIR emotion | **92.9** | 48.0 |
+| Latency, p50 per decision² | **16.7 ms** | 236–276 ms |
+
+**Tasks Tron-1B never trained on** (whole task families held out of training):
+
+| Task | Accuracy |
+|---|---|
+| IMDB sentiment | 94.7 |
+| Prompt-injection detection (deepset) | 90.5 |
+| MASSIVE intent (59 labels) | 88.4 |
+| XNLI entailment (English) | 86.7 |
+| BoolQ reading comprehension | 79.2 |
+| CLINC intent (151 labels) | 62.8 |
+| SST-5 graded sentiment | 54.8 |
+
+¹ Jev's published Banking77 result used 72 labels; Tron's uses all 77. Jev figures are its published numbers, not re-run by us.
+² Tron: one question on one NVIDIA GB10 GPU. Jev: independently measured p50 through its hosted API, which includes network time.
+
+Tron-1B was trained on the training splits of Banking77, AG News and DAIR emotion and evaluated on their official
+test splits. All results measured 2026-09-28 with troncore 1.0.
+
+## How it works
+
+Tron-1B is a 1.1B-parameter bidirectional encoder (initialised from
+[ettin-encoder-1b](https://huggingface.co/jhu-clsp/ettin-encoder-1b)) with a decision head. For each question, the
+question, the input and every option are encoded together. Each option is pooled into a vector, and a small
+attention layer compares the options with each other and with the question before they are scored. This is what lets
+it separate close labels such as "card not arrived" and "card delivery estimate". Probabilities are calibrated per
+question type and option count.
+
+It was trained on about 1.2 million typed decisions built from public classification, entailment, safety, routing,
+reasoning, preference and business-workflow datasets.
+
+## Limitations
+
+- **It decides; it does not write.** Answers are always one of the options you give it.
+- **Very large unseen label sets are its weakest area** (62.8% on CLINC's 151 intents zero-shot). For big label
+  sets, short descriptive label names help, and so does a few hundred examples of fine-tuning.
+- **Long inputs:** accuracy is best under about 2,000 tokens of input. Send the relevant section rather than a whole
+  document, or use `windows="all"`.
+- **English first.** It handles other languages, but was mostly trained and evaluated in English.
+- **Not a safety guarantee.** Use its safety and injection judgements as one layer of defence, with confidence
+  gating, not as the only one.
+
+## License
+
+The model weights are released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/): free to use,
+share and adapt for non-commercial purposes, with attribution. The `troncore` runtime is Apache-2.0. The base
+encoder is MIT-licensed; see `NOTICE`.

config.json

@@ -0,0 +1,158 @@
+{
+  "format": "troncore/1",
+  "encoder_config": {
+    "transformers_version": "5.17.0",
+    "architectures": [
+      "ModernBertForMaskedLM"
+    ],
+    "output_hidden_states": false,
+    "return_dict": true,
+    "dtype": "float32",
+    "chunk_size_feed_forward": 0,
+    "is_encoder_decoder": false,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "problem_type": null,
+    "vocab_size": 50368,
+    "hidden_size": 1792,
+    "intermediate_size": 3840,
+    "num_hidden_layers": 28,
+    "num_attention_heads": 28,
+    "hidden_activation": "gelu",
+    "max_position_embeddings": 7999,
+    "initializer_range": 0.02,
+    "initializer_cutoff_factor": 2.0,
+    "norm_eps": 1e-05,
+    "norm_bias": false,
+    "pad_token_id": 50283,
+    "eos_token_id": 50282,
+    "bos_token_id": 50281,
+    "cls_token_id": 50281,
+    "sep_token_id": 50282,
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "layer_types": [
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention"
+    ],
+    "rope_parameters": {
+      "sliding_attention": {
+        "rope_type": "default",
+        "rope_theta": 160000.0
+      },
+      "full_attention": {
+        "rope_type": "default",
+        "rope_theta": 160000.0
+      }
+    },
+    "local_attention": 128,
+    "embedding_dropout": 0.0,
+    "mlp_bias": false,
+    "mlp_dropout": 0.0,
+    "decoder_bias": true,
+    "classifier_pooling": "mean",
+    "classifier_dropout": 0.0,
+    "classifier_bias": false,
+    "classifier_activation": "gelu",
+    "deterministic_flash_attn": false,
+    "sparse_prediction": false,
+    "sparse_pred_ignore_index": -100,
+    "tie_word_embeddings": true,
+    "_name_or_path": "jhu-clsp/ettin-encoder-1b",
+    "global_attn_every_n_layers": 3,
+    "gradient_checkpointing": false,
+    "layer_norm_eps": 1e-05,
+    "model_type": "modernbert",
+    "position_embedding_type": "sans_pos",
+    "is_causal": false,
+    "causal_mask": false,
+    "output_attentions": false
+  },
+  "head": {
+    "width": 1024,
+    "mixer_layers": 2,
+    "mixer_heads": 8,
+    "dropout": 0.1,
+    "n_types": 3
+  },
+  "layout": {
+    "max_len": 4096,
+    "question_max_tokens": 128,
+    "option_max_tokens": 48,
+    "option_budget": 1536,
+    "min_state_tokens": 32
+  },
+  "calibration": {
+    "*": 1.0549,
+    "yesno": 1.0678,
+    "choice": 1.0075,
+    "score": 1.2359,
+    "yesno:2": 1.0678,
+    "choice:2": 1.0955,
+    "choice:3-5": 1.0086,
+    "choice:6-10": 1.1039,
+    "score:3-5": 1.2381,
+    "choice:11-30": 0.8095,
+    "score:6-10": 1.5212,
+    "choice:31+": 0.9561
+  },
+  "model_name": "tron-1b",
+  "val_macro": 0.8492879196203449,
+  "val_ece": 0.02179274528040747,
+  "val_accuracy": {
+    "unsafe_prompt": 0.857707509881423,
+    "commonsense_mc": 0.904,
+    "injection": 0.9783080260303688,
+    "choice": 0.7426666666666667,
+    "routing_binary": 0.8611803823773898,
+    "noul": 0.8262857142857143,
+    "knowledge_mc": 0.6967213114754098,
+    "bench_emotion": 0.9447983014861996,
+    "zeroshot": 0.9006666666666666,
+    "score": 0.6576819407008087,
+    "bench_ag_news": 0.9386331938633193,
+    "tool_choice": 0.9793103448275862,
+    "intent": 0.976063829787234,
+    "unsafe_response": 0.8703071672354948,
+    "typed_decisions_synth": 0.834,
+    "harm_category": 0.8549019607843137,
+    "routing": 0.6683291770573566,
+    "preference": 0.788135593220339,
+    "typed_decisions": 0.8,
+    "bench_banking77": 0.906060606060606
+  },
+  "trained_steps": 18582,
+  "base_model": "jhu-clsp/ettin-encoder-1b"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:143c32f44f512200ad0e0aa7b0032de199eef7013cc2419ed44cbd6a356f7e55
+size 2123836994

tokenizer_config.json

@@ -0,0 +1,17 @@
+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "is_local": true,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 8192,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "[UNK]"
+}