Question Answering
Transformers
Safetensors
English
qwen3
text-generation
rule-based reasoning
text-generation-inference
Instructions to use RuleReasoner/RuleReasoner-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RuleReasoner/RuleReasoner-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="RuleReasoner/RuleReasoner-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RuleReasoner/RuleReasoner-8B") model = AutoModelForCausalLM.from_pretrained("RuleReasoner/RuleReasoner-8B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| datasets: | |
| - RuleReasoner/rule-reasoning | |
| language: | |
| - en | |
| base_model: | |
| - Qwen/Qwen3-8B-Base | |
| pipeline_tag: question-answering | |
| library_name: transformers | |
| tags: | |
| - rule-based reasoning | |
| ## Citation | |
| 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}, | |
| author={Yang Liu and Jiaqi Li and Zilong Zheng}, | |
| year={2025}, | |
| eprint={2506.08672}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL}, | |
| url={https://arxiv.org/abs/2506.08672}, | |
| } | |
| ``` |