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license: other
license_name: s2-mas-controlled-research-v1.2
license_link: https://huggingface.co/ya9919/qwen3.5-9b-finance-lora-head-200k/blob/main/LICENSE
language:
- en
base_model: Qwen/Qwen3.5-9B
tags:
- s2-mas
- financial-qa
- decision-head
- qwen3_5
- peft
- lora
---
# qwen3.5-9b-finance-lora-head-200k
Paper: [S²-MAS](https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf). The fixed-budget adaptation results and matched downstream methods define the comparison reported here.
## Project background
S²-MAS is a society-scale multi-agent simulation framework for studying how household decisions affect consumption, cash reserves and payments. The paper connects a constructed resource of **500 million household-persona records**, learned decision policies, reusable financial scenarios and a state-transition engine. Its purchase-funding application evaluates **12 million distinct synthetic household agents** under paired conditions. Full-data Fin-Laya supplies the policy for that application; this repository supplies a fixed-budget adaptation checkpoint for the common decision-model comparison. Decision learning uses **real-survey data**. The GENESE/MAS engine implementation remains private.
The benchmark groups twelve systems into six decision-model routes: encoder option scoring; language models with learned decision heads; next-token decision SFT; dynamic-schema classification; multimodal encoder decisions; and provider decision interfaces. All receive text inputs in the common benchmark. The **17 model-repository entries** comprise **10 fixed-budget trained variants**—five native Laya and five Qwen variants—plus **six checkpoint/runtime entries** and **one compatibility alias**.
## This model's role
This checkpoint follows the **language-model plus learned decision-head route**. It applies the development-selected LoRA method to the independently initialized, official post-trained Qwen3.5-9B backbone. The 200k one-pass protocol permits a direct comparison with the adapted 4B model; the upstream training histories also differ between those initializations.
## Training design and checkpoint
- Method: `lora`.
- Distinct downstream training rows: **200,000** from the fixed decision-QA training partition.
- Initialization: [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B).
- This stage terminal row exposures: **200,000**; selected checkpoint stage exposures: **200,000**.
- Selected checkpoint cumulative row exposures: **200,000**; selected cumulative distinct rows: **200,000**.
- A fixed 20,000-example development set selects this checkpoint. The complete development partition (412,873 examples) selected LoRA on Qwen4B; the method was fixed before the independently initialized 9B run. The final test did not select budgets or methods.
## Performance and matched comparisons
**Language:** English. **Inference precision:** BF16 Qwen backbone with FP32 candidate scoring.
The common held-out test contains **240,458 QA records**, **119 question versions** and **8,275 respondent/household groups**. The checkpoint is selected using development data before this test.
| Checkpoint / reference | Accuracy (%) | Question-macro F1 (%) | Invalid decisions |
|---|---:|---:|---:|
| **This checkpoint** | **74.0919** | **36.6622** | **0** |
| Qwen4B-Base LoRA + head, same 200k protocol | 74.1435 | 36.7343 | 0 |
Accuracy weights every test record equally; invalid decisions count as incorrect. Question-macro F1 averages over every defined candidate class within each question version and then weights the 119 versions equally.
The paper reports **9B minus 4B = −0.0516 accuracy points** (95% CI −0.0981–−0.0071) and **−0.0721 question-macro F1 points** (−0.1725–0.0404). This comparison covers the independently initialized backbones under the matched downstream protocol.
## Package and inference
LoRA adapter plus a shared scalar decision head; obtain the official backbone separately.
Initialized from the official post-trained 9B release. The 4B/9B comparison varies both model scale and upstream training history.
- Model files: `head.pt, adapter/adapter_config.json, adapter/adapter_model.safetensors`.
- Reported common-test compute: BF16 Qwen backbone with FP32 candidate scoring.
- Obtain the official Qwen backbone and tokenizer named in the configuration separately.
- Inference scores the full state, question and complete candidate menu.
The supplied `qwen_decision.py` exposes the candidate-scoring inference interface. Supply the local package and the official backbone and tokenizer named in `decision_config.json`.
## License and access
The project model contribution is offered under the [S²-MAS Controlled Model Research License v1.2](LICENSE): noncommercial scientific use, no redistribution, and no unauthorized reverse engineering, extraction or distillation. Training and evaluation records are not distributed. Source-data access requires the provider's authorization. Permission requests: yli9919@hku.hk.
Project repository: [s2-mas](https://github.com/KataDavidXD/s2-mas).
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