| ---
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| license: other
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| license_name: s2-mas-controlled-research-v1.2
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| license_link: https://huggingface.co/ya9919/FinQ/blob/main/LICENSE
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| language:
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| - en
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| base_model: Qwen/Qwen3.5-4B-Base
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| tags:
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| - s2-mas
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| - financial-qa
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| - decision-head
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| - qwen3_5
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| - peft
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| - lora
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| ---
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| # FinQ · Qwen3.5-4B-Base + LoRA + decision head · 200k QA
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| ## Model naming and experiment labels
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| **FinQ — Qwen3.5-4B-Base + LoRA + learned decision head, 200k distinct QA** names this selected best checkpoint. [**FinL**](https://huggingface.co/ya9919/FinL) is the parallel full English-base six-round encoder route, and [**FinL-mlx**](https://huggingface.co/ya9919/FinL-mlx) is its MLX FP16 runtime. The original upstream model remains **Qwen/Qwen3.5-4B-Base**. Head-only, 50k and 9B Qwen checkpoints retain their explicit comparator names.
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| 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.
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| ## Project background
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| 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 FinL 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.
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| 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**.
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| ## This model's role
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| **FinQ** follows the **language-model plus learned decision-head route**. LoRA adapters and a shared scalar decision head are trained together on Qwen3.5-4B-Base. The 200k head-only counterpart holds the backbone fixed, allowing the comparison to assess representation adaptation. The complete development partition selects this LoRA method before the separate 9B run.
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| ## Training design and checkpoint
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| - Method: `lora`.
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| - Distinct downstream training rows: **200,000** from the fixed decision-QA training partition.
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| - Initialization: [Qwen/Qwen3.5-4B-Base](https://huggingface.co/Qwen/Qwen3.5-4B-Base).
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| - This stage terminal row exposures: **200,000**; selected checkpoint stage exposures: **200,000**.
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| - Selected checkpoint cumulative row exposures: **200,000**; selected cumulative distinct rows: **200,000**.
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| - A fixed 20,000-example development set selects this checkpoint. The complete 412,873-example development partition compares the two 200k Qwen4B adaptation methods.
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| ## Performance and matched comparisons
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| **Language:** English. **Inference precision:** BF16 Qwen backbone with FP32 candidate scoring.
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| 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.
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| | Checkpoint / reference | Accuracy (%) | Question-macro F1 (%) | Invalid decisions |
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| |---|---:|---:|---:|
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| | **FinQ · Qwen3.5-4B-Base + LoRA + head · 200k QA** | **74.1435** | **36.7343** | **0** |
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| | Qwen4B head only, same 200k unique QA | 67.2841 | 30.8995 | 0 |
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| 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.
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| The head-only reference uses the same pretrained backbone, unique examples and common test, with its backbone fixed. This checkpoint also adapts the backbone representation through LoRA.
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| ## Package and inference
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| LoRA adapter plus a shared scalar decision head; obtain the official backbone separately.
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| The 4B initialization is the official pre-trained-only Base release.
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| - Model files: `head.pt, adapter/adapter_config.json, adapter/adapter_model.safetensors`.
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| - Reported common-test compute: BF16 Qwen backbone with FP32 candidate scoring.
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| - Obtain the official Qwen backbone and tokenizer named in the configuration separately.
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| - Inference scores the full state, question and complete candidate menu.
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| 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`.
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| ## License and access
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| 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.
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| Project repository: [s2-mas](https://github.com/KataDavidXD/s2-mas).
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