| ---
|
| language:
|
| - en
|
| license: other
|
| license_name: s2-mas-controlled-research-v1.2
|
| license_link: https://huggingface.co/ya9919/fin-laya-3-rounds/blob/main/LICENSE
|
| base_model: convaiinnovations/laya
|
| base_model_relation: finetune
|
| tags:
|
| - s2-mas
|
| - financial-one-shot
|
| - decision-model
|
| ---
|
|
|
| # FinL · three-round comparator (native PyTorch)
|
|
|
| ## Model naming and experiment labels
|
|
|
| This is the **FinL three-round comparator**, in native Laya/PyTorch format. The main **FinL** name refers to the [full six-round checkpoint](https://huggingface.co/ya9919/FinL). Its paired earlier MLX format remains [fin-laya-mlx-3-rounds](https://huggingface.co/ya9919/fin-laya-mlx-3-rounds).
|
|
|
| ## Project background
|
|
|
| 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.
|
|
|
| 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.
|
|
|
| ## Repository role and training
|
|
|
| This is the **native FinL 3-round financial checkpoint**, with approximately 421M parameters, based on Laya's ModernBERT-large encoder and option-scoring head. Two SFT stages followed by native RLCD adapt the model to real-survey data, using **1,721,623 training observations per language**. The [MLX runtime](https://huggingface.co/ya9919/fin-laya-mlx-3-rounds) packages this trained checkpoint for FP16 deployment. The [6-round continuation](https://huggingface.co/ya9919/FinL) extends its supervised task coverage.
|
|
|
| ## Paper results for this checkpoint
|
|
|
| | Test | Input language | Backend / precision | QA denominator | Question versions | Accuracy | Question-macro F1 |
|
| |---|---|---|---:|---:|---:|---:|
|
| | Shared financial questions | English | PyTorch BF16 | 240,458 | 119 | 74.5336% | 38.0164% |
|
| | Expanded test | English | PyTorch BF16 | 418,597 | 862 | 56.3657% | 19.2395% |
|
|
|
| Accuracy weights QA records equally. Question-macro F1 averages F1 over the complete candidate classes within each question version, then weights question versions equally.
|
|
|
| On the same shared test, original Laya in native BF16 scores 60.2080% accuracy and 29.5574% F1; financial training raises these to the 3-round results above. See Figure 5 and Methods M4/M6.
|
|
|
| ## Deployment timing
|
|
|
| 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.
|
|
|
| ## Model files and inference
|
|
|
| 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.
|
|
|
| ## License and manuscript
|
|
|
| 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.
|
|
|
| [S²-MAS manuscript](https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf) · [Project](https://github.com/KataDavidXD/s2-mas)
|
|
|
| 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.
|
|
|
