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.

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. 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 MLX FP16 runtime of bilingual FinL-bilingual, paired with the native checkpoint. 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.

Paper and licensing

Methods and results are described in the S²-MAS manuscript.

The project model contribution is governed by 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