S²-MAS research release

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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 +1,223 @@
+S²-MAS Controlled Model Research License, version 1.2
+19 October 2026
+
+Copyright 2026 S²-MAS authors, to the extent of rights held in the project contributions.
+
+1. Scope. This prospective distribution offer applies to the project-owned model contributions offered in this repository and its derivative model distribution. It changes no model parameters, training or evaluation. By accepting and exercising the permissions in this offer, the recipient agrees to the following conditions.
+
+2. Noncommercial research. The recipient may copy, store, load and run the supplied model locally for noncommercial scientific research and evaluation, and publish attributed, non-identifying aggregate findings. Commercial use requires prior written permission. Commercial use includes paid products or services, commercial deployment, consulting, business operations or decisions, and developing models or products for commercial distribution. Commercial entities require separate written permission.
+
+3. No redistribution. Without prior written permission, do not sell, rent, sublicense, share, mirror, rehost, publicly upload or distribute the covered material, substantial parameter extracts or model derivatives. Do not expose it to third parties through an API, endpoint, hosted demo or model service. Each recipient obtains permission from the authors.
+
+4. No unauthorized reverse engineering. Without prior written permission, do not reverse engineer, decompile, disassemble, reconstruct, extract, imitate or distill the covered project model; perform model inversion or parameter-extraction attacks; recover its parameters or training records through probes or outputs; or circumvent access controls. Do not reconstruct confidential source records or identify individuals. Reading and loading the supplied weights as needed for permitted evaluation is allowed. Non-waivable statutory exceptions remain effective.
+
+5. Other permissions. Training, fine-tuning, weight modification, model integration, distillation or derivative distribution requires separate written permission. Preserve this license and applicable upstream notices. Cite the model name and version when reporting research. No training records or source-data access is granted; obtain restricted inputs through their authorized providers. Wikidata structured facts retain CC0 independently.
+
+6. Breach. Breach terminates permissions granted solely by this offer. Stop use and remove copies held only under it, except for mandatory retention. Independent rights continue. Other rights are reserved. The material is provided as is, without warranty, to the extent permitted by law. This is a restricted research-use offer, not an OSI open-source license.
+
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Model_Card_EN.md

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+---
+language:
+- en
+- zh
+license: other
+license_name: s2-mas-controlled-research-v1.2
+license_link: https://huggingface.co/ya9919/fin-laya-multilingual/blob/main/LICENSE
+tags:
+- s2-mas
+- financial-one-shot
+- decision-model
+---
+# Fin-Laya Multilingual
+
+A structured financial decision model for S²-MAS. Training and evaluation use real-survey data; those records are not distributed.
+
+## Inference
+
+Approximately 322M parameters. Runtime: native Laya / PyTorch. Use `predict_example.py`, `example_request.json`, and caller-provided states with complete choice criteria. The packaged example is an independent synthetic input.
+
+Paired format: [MLX](https://huggingface.co/ya9919/fin-laya-multilingual-mlx).
+
+## Paper and licensing
+
+Methods and results are described in the [S²-MAS manuscript](https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf). This repository does not distribute question-level results, training records, or data-source metadata.
+
+The project model contribution is governed by [LICENSE](LICENSE), which retains the original Apache-2.0 appendix and independent rights. The public project provides scientific plotting, decision-model training and evaluation, and independent synthetic companion-data generation scripts. The GENESE/MAS engine implementation remains private.
+
+[Project](https://github.com/KataDavidXD/s2-mas)

Model_Card_ZH.md

@@ -0,0 +1,29 @@
+---
+language:
+- en
+- zh
+license: other
+license_name: s2-mas-controlled-research-v1.2
+license_link: https://huggingface.co/ya9919/fin-laya-multilingual/blob/main/LICENSE
+tags:
+- s2-mas
+- financial-one-shot
+- decision-model
+---
+# Fin-Laya Multilingual
+
+本仓库提供 S²-MAS 的金融结构化决策模型。训练与评测使用 real-survey 数据；相关记录不分发。
+
+## 推理
+
+模型约 322M 参数。运行环境：原生 Laya / PyTorch。使用 `predict_example.py`、`example_request.json` 和调用者自备的状态及完整选项进行推理。示例是独立合成输入。
+
+配套格式：[MLX 格式](https://huggingface.co/ya9919/fin-laya-multilingual-mlx)。
+
+## 论文与许可
+
+研究方法与结果见 [S²-MAS manuscript](https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf)。本仓库不包含题目级结果、训练记录或来源元数据。
+
+项目模型贡献适用 [LICENSE](LICENSE)，其中保留原始 Apache-2.0 附录与独立权利。公开项目提供科学制图、决策模型训练与评测，以及独立合成配套数据生成脚本；GENESE／MAS 引擎实现保持私有。
+
+[项目入口](https://github.com/KataDavidXD/s2-mas)

NOTICE

@@ -0,0 +1,3 @@
+Derived from Convai Innovations Laya multilingual, Apache-2.0.
+MLX conversion uses mizorewww/laya-mlx, Apache-2.0.
+Survey data are not distributed and retain their source-provider terms.

README.md

@@ -0,0 +1,29 @@
+---
+language:
+- en
+- zh
+license: other
+license_name: s2-mas-controlled-research-v1.2
+license_link: https://huggingface.co/ya9919/fin-laya-multilingual/blob/main/LICENSE
+tags:
+- s2-mas
+- financial-one-shot
+- decision-model
+---
+# Fin-Laya Multilingual
+
+A structured financial decision model for S²-MAS. Training and evaluation use real-survey data; those records are not distributed.
+
+## Inference
+
+Approximately 322M parameters. Runtime: native Laya / PyTorch. Use `predict_example.py`, `example_request.json`, and caller-provided states with complete choice criteria. The packaged example is an independent synthetic input.
+
+Paired format: [MLX](https://huggingface.co/ya9919/fin-laya-multilingual-mlx).
+
+## Paper and licensing
+
+Methods and results are described in the [S²-MAS manuscript](https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf). This repository does not distribute question-level results, training records, or data-source metadata.
+
+The project model contribution is governed by [LICENSE](LICENSE), which retains the original Apache-2.0 appendix and independent rights. The public project provides scientific plotting, decision-model training and evaluation, and independent synthetic companion-data generation scripts. The GENESE/MAS engine implementation remains private.
+
+[Project](https://github.com/KataDavidXD/s2-mas)

encoder/config.json

@@ -0,0 +1,80 @@
+{
+  "architectures": [
+    "ModernBertForMaskedLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 1,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 1,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "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"
+  ],
+  "local_attention": 128,
+  "local_rope_theta": 160000.0,
+  "mask_token_id": 4,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "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": {
+    "full_attention": {
+      "rope_theta": 160000,
+      "rope_type": "default"
+    },
+    "sliding_attention": {
+      "rope_theta": 160000,
+      "rope_type": "default"
+    }
+  },
+  "sep_token_id": 1,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.57.6",
+  "vocab_size": 256000
+}

example_request.json

@@ -0,0 +1,21 @@
+{
+  "state": {
+    "household": {
+      "income_monthly": 5000,
+      "essential_spending": 3500,
+      "cash": 1500
+    },
+    "event": "A temporary expense reduces cash available this month."
+  },
+  "questions": {
+    "decision01": {
+      "type": "choice",
+      "instructions": "Choose the household discretionary spending plan for this month.",
+      "criteria": {
+        "A": "Spend 500 while maintaining essential spending.",
+        "B": "Spend 250 while maintaining essential spending.",
+        "C": "Defer discretionary spending while maintaining essential spending."
+      }
+    }
+  }
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71961318b75c108cf03e243a7d24df28f938e36d1e4f042ea124c357ef52ba53
+size 643835524

predict_example.py

@@ -0,0 +1,26 @@
+"""Run an independent synthetic structured-choice example."""
+import argparse
+import json
+from pathlib import Path
+
+parser = argparse.ArgumentParser(description=__doc__)
+parser.add_argument('--backend', choices=('torch', 'mlx'), default='torch')
+parser.add_argument('--model', type=Path, default=Path(__file__).resolve().parent)
+parser.add_argument('--request', type=Path)
+parser.add_argument('--device')
+parser.add_argument('--output', type=Path)
+args = parser.parse_args()
+request = json.loads((args.request or args.model / 'example_request.json').read_text(encoding='utf-8'))
+if args.backend == 'mlx':
+    from laya_mlx.agent import Agent
+    agent = Agent(str(args.model), dtype='float32', device=args.device)
+else:
+    import torch
+    from laya.agent import Agent
+    torch.set_num_threads(4)
+    agent = Agent(str(args.model), device=args.device or 'cuda', compile=False)
+result = agent.predict(request['state'], request['questions'])
+serialized = json.dumps(result, ensure_ascii=False, indent=2) + '\n'
+if args.output:
+    args.output.write_text(serialized, encoding='utf-8')
+print(serialized)

requirements.txt

@@ -0,0 +1,2 @@
+git+https://github.com/NandhaKishorM/laya.git@68804629e8ccd9d616d48a40e87de9fabbeae069
+huggingface_hub

rl_agent_config.json

@@ -0,0 +1,24 @@
+{
+  "encoder": "jhu-clsp/mmBERT-base",
+  "head_layers": 2,
+  "max_len": 512,
+  "head_max_len": 256,
+  "max_prefixes": 6,
+  "act_costs": {
+    "escalate": 0.5
+  },
+  "cost_wrong_act": 3.0,
+  "amp_dtype": "bf16",
+  "model_name": "fin-laya-multilingual",
+  "temperature": [
+    1.0483462810516357,
+    1.0,
+    1.0
+  ],
+  "temperature_by_options": {
+    "choice:3-5": 1.04791259765625,
+    "choice:2": 1.05064058303833,
+    "choice:6-10": 1.0429390668869019
+  },
+  "fine_tuned": true
+}

tokenizer/special_tokens_map.json

@@ -0,0 +1,53 @@
+{
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "extra_0": "<start_of_turn>",
+  "extra_1": "<end_of_turn>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
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