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README.md

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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.