Instructions to use RuleReasoner/RuleReasoner-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RuleReasoner/RuleReasoner-4B with Transformers:

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

pipe = pipeline("text-generation", model="RuleReasoner/RuleReasoner-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RuleReasoner/RuleReasoner-4B")
model = AutoModelForCausalLM.from_pretrained("RuleReasoner/RuleReasoner-4B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RuleReasoner/RuleReasoner-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RuleReasoner/RuleReasoner-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RuleReasoner/RuleReasoner-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RuleReasoner/RuleReasoner-4B

SGLang

How to use RuleReasoner/RuleReasoner-4B 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 "RuleReasoner/RuleReasoner-4B" \
    --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": "RuleReasoner/RuleReasoner-4B",
		"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 "RuleReasoner/RuleReasoner-4B" \
        --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": "RuleReasoner/RuleReasoner-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RuleReasoner/RuleReasoner-4B with Docker Model Runner:
```
docker model run hf.co/RuleReasoner/RuleReasoner-4B
```

RuleReasoner

nielsr HF Staff commited on Dec 9, 2025

Commit

89b0d0e

verified ·

1 Parent(s): 1643a1a

Improve pipeline tag and add link to code (#1)

Browse files

- Improve pipeline tag and add link to code (2e0f0e953f2614545753958cfe4be46db0c83b3c)

Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +11 -7

README.md CHANGED Viewed

@@ -1,20 +1,22 @@
 ---
-license: mit
 datasets:
 - RuleReasoner/rule-reasoning
 language:
 - en
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen3-4B-Base
-new_version: RuleReasoner/RuleReasoner-4B
-pipeline_tag: question-answering
-library_name: transformers
 tags:
 - rule-based reasoning
 ---
 If you use the model in your research, please cite the original papers as below.
 ```latex
 @article{liu2025rulereasoner,
       title={RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling},
@@ -25,4 +27,6 @@ If you use the model in your research, please cite the original papers as below.
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2506.08672},
 }
-```

 ---
+base_model:
+- Qwen/Qwen3-4B-Base
 datasets:
 - RuleReasoner/rule-reasoning
 language:
 - en
+library_name: transformers
+license: mit
 metrics:
 - accuracy
+pipeline_tag: text-generation
 tags:
 - rule-based reasoning
+new_version: RuleReasoner/RuleReasoner-4B
 ---
 If you use the model in your research, please cite the original papers as below.
 ```latex
 @article{liu2025rulereasoner,
       title={RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling},
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2506.08672},
 }
+```
+Code: https://github.com/bigai-nlco/RuleReasoner