| ---
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| language:
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| - en
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| license: other
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| license_name: s2-mas-controlled-research-v1.2
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| license_link: https://huggingface.co/ya9919/FinL/blob/main/LICENSE
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| base_model: convaiinnovations/laya
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| base_model_relation: finetune
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| tags:
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| - s2-mas
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| - financial-one-shot
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| - decision-model
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| ---
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| # FinL · full six-round checkpoint (native PyTorch)
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| ## Model naming and experiment labels
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| **FinL — full English-base six-round checkpoint, native Laya/PyTorch** is the main encoder decision-model route. [**FinL-mlx**](https://huggingface.co/ya9919/FinL-mlx) is its MLX FP16 runtime. [**FinQ**](https://huggingface.co/ya9919/FinQ) is the parallel Qwen3.5-4B-Base route with LoRA plus a learned decision head, trained on 200,000 distinct QA records.
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| ## Project background
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| S²-MAS is a society-scale multi-agent simulation framework for studying how heterogeneous household decisions become population outcomes under changing conditions. It connects a constructed resource of **500 million household personas**, reusable scenarios, trainable decision models and stateful world updates. FinL supplies a reusable decision policy: it scores the complete alternatives for a household state and question, allowing the same policy interface to serve different tasks.
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| The manuscript's paired-condition application evaluates **12 million distinct synthetic household agents and 24 million choices**. These findings connect individual choices to consumption, cash and payment paths over time.
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| ## Repository role and training
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| This is the **native English-base FinL 6-round checkpoint**, with approximately 421M parameters. It continues the [3-round financial checkpoint](https://huggingface.co/ya9919/fin-laya-3-rounds) with SFT and RLCD over **2,925,263 training examples per language**, adapting Laya's ModernBERT-large encoder and option-scoring head to expanded supervision from real-survey data. This is the largest disclosed domain post-training set among the financial decision models compared in the manuscript.
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| The [MLX runtime](https://huggingface.co/ya9919/FinL-mlx) deploys the same trained checkpoint family in FP16 and is the format used in the manuscript's English purchase-funding applications.
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| ## Paper results for this checkpoint
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| | Test | Input language | Backend / precision | QA denominator | Question versions | Accuracy | Question-macro F1 |
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| |---|---|---|---:|---:|---:|---:|
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| | Shared financial questions | English | PyTorch BF16 | 240,458 | 119 | 74.5586% | 38.40% |
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| | Expanded test | English | PyTorch BF16 | 418,597 | 862 | 67.2728% | 27.8355% |
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| Accuracy weights QA records equally. Question-macro F1 averages F1 over the complete candidate classes within each question version, then weights question versions equally.
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| Native shared-test F1 is shown at the precision displayed in Figure 5; Methods M6 reports the expanded test. The same checkpoint family in MLX FP16 scores 74.5544% accuracy and 38.3746% F1 on the shared test; see the [MLX card](https://huggingface.co/ya9919/FinL-mlx) and Methods M5.
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| ## Deployment timing
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| The manuscript's single-request timing uses the [FinL-bilingual checkpoint](https://huggingface.co/ya9919/fin-laya-multilingual-mlx) on one NVIDIA A100 PCIe 40GB in FP16, with batch size and concurrency one and three fixed Chinese cases. Its card and Methods M9 report latency, throughput and the Jev comparison.
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| ## Model files and inference
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| This repository contains model weights, the tokenizer and the inference configuration. Native Laya / PyTorch inference uses BF16. `predict_example.py` uses an independently generated synthetic example.
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| ## License and manuscript
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| The project model contribution is offered under the [Controlled Model Research License v1.2](LICENSE). The full license and retained upstream Apache-2.0 rights are in LICENSE. Training and evaluation records are not distributed with the model.
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| [S²-MAS manuscript](https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf) · [Project](https://github.com/KataDavidXD/s2-mas)
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| 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.
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