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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
---
# FinL-bilingual 路 native PyTorch
## Model naming and experiment labels
**FinL-bilingual** is the bilingual initial-supervision checkpoint, with a multilingual encoder and English/Chinese support. Its MLX runtime is **FinL-bilingual-mlx**. The paper's A100 single-request timing protocol and 22.72脳 Jev shared-input-token-throughput result belong to this bilingual checkpoint pair. The main English-base six-round checkpoints are [FinL](https://huggingface.co/ya9919/FinL) and [FinL-mlx](https://huggingface.co/ya9919/FinL-mlx).
## Project background
S虏-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-bilingual 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 bilingual FinL-bilingual checkpoint**, adapted from multilingual Laya's approximately 322M-parameter mmBERT encoder and option-scoring head. It supports English and Chinese inputs and uses **1,721,623 training observations per language** from real-survey data, with two SFT stages and native RLCD. It is the initial-supervision bilingual release, paired with the [MLX runtime](https://huggingface.co/ya9919/fin-laya-multilingual-mlx).
## Paper results for this checkpoint
| Test | Input language | Backend / precision | QA denominator | Question versions | Accuracy | Question-macro F1 |
|---|---|---|---:|---:|---:|---:|
| Shared financial questions | Chinese | PyTorch BF16 | 240,458 | 119 | 74.52% | 37.99% |
Accuracy weights QA records equally. Question-macro F1 averages F1 over the complete candidate classes within each question version, then weights question versions equally.
Figure 5 reports the Chinese shared test to two decimal places: accuracy improves by 21.61 percentage points and F1 by 10.60 points over the multilingual base.
## Deployment timing
The **11 October 2026** deployment test uses this bilingual checkpoint on one **NVIDIA A100 PCIe 40GB**, with local models in **FP16**, **batch size and concurrency one**. Each of three fixed Chinese financial-choice cases receives three warm-ups and twenty timed repetitions, for sixty timed calls per arm.
| Bilingual checkpoint deployment / reference | Mean latency (ms) | Choices/s | Shared input tokens/s |
|---|---:|---:|---:|
| FinL-bilingual 路 PyTorch FP16 | 22.06 | 45.33 | 15,247.34 |
| FinL-bilingual-mlx 路 MLX CUDA FP16 | 9.10 | 109.88 | 36,955.47 |
| Jev 1.13 路 API | 206.77 | 4.84 | 1,626.63 |
MLX delivers **22.72 times Jev API's shared-input-token throughput** and **2.42 times the same checkpoint's PyTorch throughput**. Timing includes input preparation, tokenization, synchronized prediction and response formatting; loading is excluded and Jev includes its network round trip. Shared input tokens are counted with the mmBERT tokenizer on identical serialized requests. Jev's server hardware and precision are provider-managed. Methods M9 and Table S5 give the protocol and results.
## 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 [S虏-MAS manuscript](https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf).
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)
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