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Model: yongqiqng/AutoBM-Seed-Coder-8B-R
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---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
base_model: ByteDance-Seed/Seed-Coder-8B-Reasoning
tags:
- code
- structural-engineering
- openseespy
- scientific-modeling
- reinforcement-learning
- grpo
- autobm
---
# AutoBM-Seed-Coder-8B-R
Official model release for the paper *Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation*.
This model is trained from [`ByteDance-Seed/Seed-Coder-8B-Reasoning`](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Reasoning) via the **RLA-SPC** two-stage alignment strategy:
- **Stage I — Domain Instruction Fine-Tuning (SFT)** on the CivilInstruct dataset (10,912 samples).
- **Stage II — Self-Play Constraint GRPO (SPC-GRPO)** with the Multi-Granularity Hybrid Reward (MGHR), combining format, AST, and OpenSeesPy execution rewards.
The resulting model generates **executable, physically consistent OpenSeesPy structural modeling code** from natural language building specifications.
## BMEval Results
| Model | Pass@1 | Pass@5 | Pass@5_period | Pass@5_compliance | Pass@5_strict | Overall Avg |
|-------|--------|--------|---------------|-------------------|---------------|-------------|
| Seed-Coder-8B-R (baseline) | 11.72 | 21.09 | 0.78 | 3.13 | 0.78 | 6.51 |
| **AutoBM-Seed-Coder-8B-R (this model)** | **64.18** | **97.28** | **78.05** | **92.47** | **77.14** | **81.95** |
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "yongqiqng/AutoBM-Seed-Coder-8B-R"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
prompt = '''Generate OpenSeesPy code to model a 5-story reinforced concrete frame building:
- Floor height: 3.5 m
- Bay width: 6 m (3 bays in X, 2 bays in Y)
- Seismic intensity: 0.2g
Compute the fundamental period.'''
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=4096, temperature=0.6, top_p=0.95, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
```
## Training Details
| Stage | Method | Data |
|-------|--------|------|
| Stage I | Supervised Fine-Tuning | CivilInstruct SFT (9,894 train + 202 val) |
| Stage II | SPC-GRPO with MGHR | CivilInstruct RL (455 train + 57 test) |
The MGHR reward function combines:
- `r_fmt` (Format, weight 0.05) — `<think>...</think><answer>...</answer>` structure
- `r_ast` (AST, weight 0.25) — three-tiered OpenSeesPy API coverage
- `r_exec` (Execution, weight 0.70) — sandboxed OpenSeesPy execution + period error grading
See the [paper](https://arxiv.org/abs/2602.07083) and [training code](https://github.com/Jovanqing/AutoBM) for details.
## Related
- Paper: [arXiv:2602.07083](https://arxiv.org/abs/2602.07083)
- Code: [github.com/Jovanqing/AutoBM](https://github.com/Jovanqing/AutoBM)
- Sample data: [yongqiqng/CivilInstruct-Sample](https://huggingface.co/datasets/yongqiqng/CivilInstruct-Sample)
- Base model: [ByteDance-Seed/Seed-Coder-8B-Reasoning](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Reasoning)
## Citation
```bibtex
@article{jiang2026rethinking,
title={Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation},
author={Jiang, Yongqing and Wang, Jianze and Shen, Zhiqi and Lin, Zhenghong and Wang, Jiayuan and Yang, Yijian and Dai, Kaoshan and Luo, Haoran},
journal={arXiv preprint arXiv:2602.07083},
year={2026}
}
```
## License
Released under the Apache 2.0 License, consistent with the base Seed-Coder-8B-Reasoning model.

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{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set role = message['role'] %}{{ bos_token + role + '
' + message['content'] | trim + eos_token }}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant
'}}{% endif %}

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"vocab_size": 155136
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