Instructions to use yongqiqng/AutoBM-Seed-Coder-8B-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yongqiqng/AutoBM-Seed-Coder-8B-R with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="yongqiqng/AutoBM-Seed-Coder-8B-R")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yongqiqng/AutoBM-Seed-Coder-8B-R")
model = AutoModelForCausalLM.from_pretrained("yongqiqng/AutoBM-Seed-Coder-8B-R", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yongqiqng/AutoBM-Seed-Coder-8B-R with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yongqiqng/AutoBM-Seed-Coder-8B-R"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yongqiqng/AutoBM-Seed-Coder-8B-R",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yongqiqng/AutoBM-Seed-Coder-8B-R

SGLang

How to use yongqiqng/AutoBM-Seed-Coder-8B-R with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "yongqiqng/AutoBM-Seed-Coder-8B-R" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yongqiqng/AutoBM-Seed-Coder-8B-R",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "yongqiqng/AutoBM-Seed-Coder-8B-R" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yongqiqng/AutoBM-Seed-Coder-8B-R",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yongqiqng/AutoBM-Seed-Coder-8B-R with Docker Model Runner:
```
docker model run hf.co/yongqiqng/AutoBM-Seed-Coder-8B-R
```

AutoBM-Seed-Coder-8B-R / 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.