---
language:
- en
- zh
license: other
license_name: s2-mas-controlled-research-v1.2
license_link: https://huggingface.co/ya9919/fin-laya-multilingual-mlx/blob/main/LICENSE
tags:
- s2-mas
- financial-one-shot
- decision-model
---

# FinL-bilingual-mlx · MLX CUDA FP16

## Model naming and experiment labels

**FinL-bilingual-mlx** is the MLX FP16 runtime of the bilingual initial-supervision checkpoint, **FinL-bilingual**. The paper's A100 single-request timing protocol and 22.72× Jev shared-input-token-throughput result belong to this bilingual checkpoint pair. The main English-base six-round MLX runtime is [FinL-mlx](https://huggingface.co/ya9919/FinL-mlx).

## 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-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 **MLX FP16 runtime of bilingual FinL-bilingual**, paired with the [native checkpoint](https://huggingface.co/ya9919/fin-laya-multilingual). The approximately 322M-parameter model adapts multilingual Laya's mmBERT encoder and option-scoring head to real-survey data, with two SFT stages and native RLCD on **1,721,623 training observations per language**. It supports English and Chinese inputs. Native and MLX packages provide runtime formats for the same bilingual trained checkpoint.

## Paper results for this checkpoint

| Test | Input language | Backend / precision | QA denominator | Question versions | Accuracy | Question-macro F1 |
|---|---|---|---:|---:|---:|---:|
| Paired native checkpoint reference | 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.

The table is the paired trained checkpoint's native PyTorch BF16 reference; the MLX FP16 single-request timing is reported below. 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 · PyTorch FP16 | 22.06 | 45.33 | 15,247.34 |
| FinL-bilingual-mlx · MLX CUDA FP16 | 9.10 | 109.88 | 36,955.47 |
| Jev 1.13 · 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: Laya-MLX / MLX CUDA, FP16 deployment. 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: [native](https://huggingface.co/ya9919/fin-laya-multilingual).

## 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). 

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)