Instructions to use AIOR-Research/SOLID-StepORLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIOR-Research/SOLID-StepORLM with Transformers:

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

pipe = pipeline("text-generation", model="AIOR-Research/SOLID-StepORLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AIOR-Research/SOLID-StepORLM")
model = AutoModelForCausalLM.from_pretrained("AIOR-Research/SOLID-StepORLM", 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 AIOR-Research/SOLID-StepORLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AIOR-Research/SOLID-StepORLM

SGLang

How to use AIOR-Research/SOLID-StepORLM 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 "AIOR-Research/SOLID-StepORLM" \
    --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": "AIOR-Research/SOLID-StepORLM",
		"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 "AIOR-Research/SOLID-StepORLM" \
        --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": "AIOR-Research/SOLID-StepORLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIOR-Research/SOLID-StepORLM with Docker Model Runner:
```
docker model run hf.co/AIOR-Research/SOLID-StepORLM
```

SOLID-StepORLM

This is the checkpoint of SOLID (Solver-Informed Self-Distillation) built from Chenyu-Zhou/StepORLM-Qwen3-8B for operations-research modeling and solver-backed answer generation.

The model was trained with GRPO and solver-informed token-level KL supervision. It uses the COPT-style StepORLM response template.

Evaluation

Each problem was sampled 64 times. maj@64 is majority-vote accuracy; pass@k uses the unbiased pass-at-k estimator.

Dataset	maj@64	pass@1	pass@2	pass@4
OptMATH	31.33	18.25	24.40	30.28
MAMO-Complex	70.44	66.43	71.58	74.79
InOR	48.00	39.81	46.07	50.59

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AIOR-Research/SOLID-StepORLM"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

The generated optimization code expects a compatible COPT environment for execution.

Citation

If you use SOLID in your research, please cite:

@misc{zhu2026verifiedanswerssolverinformedselfdistillation,
      title={Beyond Verified Answers: Solver-Informed Self-Distillation for Bootstrapping Operations Research Language Models},
      author={Rui Zhu and Minglong Cao and Chenyu Zhou and Jianghao Lin and Dongdong Ge},
      year={2026},
      eprint={2609.09957},
      archivePrefix={arXiv},
      primaryClass={math.OC},
      url={https://arxiv.org/abs/2609.09957},
}

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for AIOR-Research/SOLID-StepORLM

Base model

Chenyu-Zhou/StepORLM-Qwen3-8B

Finetuned

(2)

this model

Paper for AIOR-Research/SOLID-StepORLM

Beyond Verified Answers: Solver-Informed Self-Distillation for Bootstrapping Operations Research Language Models

Paper • 2609.09957 • Published 1 day ago