language:
- en
license: other
license_name: s2-mas-controlled-research-v1.2
license_link: https://huggingface.co/ya9919/FinL/blob/main/LICENSE
base_model: convaiinnovations/laya
base_model_relation: finetune
tags:
- s2-mas
- financial-one-shot
- decision-model
FinL · full six-round checkpoint (native PyTorch)
Model naming and experiment labels
FinL — full English-base six-round checkpoint, native Laya/PyTorch is the main encoder decision-model route. FinL-mlx is its MLX FP16 runtime. FinQ is the parallel Qwen3.5-4B-Base route with LoRA plus a learned decision head, trained on 200,000 distinct QA records.
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. A 24-month extension follows 10,000 households through changing income, expenses and bank offers. The applications show how household resources shape consumption responses: persistent income loss draws down reserves and delays purchases, while sustained income gains support both cash reserves and purchases. Repayment feedback affects later offers. These findings connect individual choices to consumption, cash and payment paths over time.
Repository role and training
This is the native English-base FinL 6-round checkpoint, with approximately 421M parameters. It continues the 3-round financial checkpoint 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.
The MLX runtime deploys the same trained checkpoint family in FP16 and is the format used in the manuscript's English purchase-funding applications.
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 | 74.5586% | 38.40% |
| Expanded test | English | PyTorch BF16 | 418,597 | 862 | 67.2728% | 27.8355% |
Accuracy weights QA records equally. Question-macro F1 averages F1 over the complete candidate classes within each question version, then weights question versions equally.
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 and Methods M5.
Deployment timing
The manuscript's single-request timing uses the FinL-bilingual checkpoint 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. The full license and retained upstream Apache-2.0 rights are in LICENSE. Training and evaluation records are not distributed with the model.
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.