988887d6f4
Model: OpenLearnLM/special-r1-deepseek-qwen3-8b-sped-adaptive-think-noreward Source: Original Platform
99 lines
2.7 KiB
Markdown
99 lines
2.7 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-7B-Instruct
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tags:
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- reinforcement-learning
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- GRPO
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- reasoning
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- education
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- qwen2.5
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language:
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- en
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- ko
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Special-R1-Qwen2.5-7B-NoThink
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A reasoning-enhanced language model fine-tuned from Qwen2.5-7B-Instruct using GRPO (Group Relative Policy Optimization) for special education applications.
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## Model Description
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This model is trained to provide direct, concise answers without explicit chain-of-thought reasoning steps (NoThink variant). It focuses on generating accurate responses efficiently.
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- **Base Model**: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
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- **Training Method**: GRPO (Group Relative Policy Optimization)
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- **Training Steps**: 300
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- **Focus**: Direct answer generation without verbose reasoning
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## Training Details
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### Training Configuration
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- **Framework**: veRL (Volcano Engine Reinforcement Learning)
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- **Algorithm**: GRPO
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- **Batch Size**: Configured for 4x GPU setup
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- **Precision**: bfloat16
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### Training Data
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- Educational reasoning tasks
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- Mathematical problem solving
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- General knowledge Q&A
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "OpenLearnLM/special-r1-qwen2.5-7b-nothink"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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messages = [
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{"role": "user", "content": "What is the capital of France?"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512)
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print(response)
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```
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## Model Variants
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| Model | Description |
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|-------|-------------|
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| **special-r1-qwen2.5-7b-nothink** (this) | Direct answers without explicit reasoning |
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| special-r1-qwen2.5-7b-think | With chain-of-thought reasoning |
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## Limitations
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- Trained primarily on English and Korean data
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- May not perform optimally on highly specialized domains outside training distribution
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- As an early checkpoint (step 300), performance may improve with continued training
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## Citation
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If you use this model, please cite:
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```bibtex
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@misc{openlearnlm2025special,
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title={Special-R1: Reasoning Models for Education},
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author={OpenLearnLM Team},
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year={2025},
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publisher={HuggingFace},
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url={https://huggingface.co/OpenLearnLM/special-r1-qwen2.5-7b-nothink}
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}
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```
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## License
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This model is released under the Apache 2.0 License, following the base model's license.
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