Create README.md

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation
+- nlp
+datasets:
+- DeepMath103K
+metrics:
+- avg@1 / pass@k
+base_model:
+- Deepseek-R1-Distill-Qwen-1.5B
+pipeline_tag: text-generation
+library_name: transformers
+arxiv: 2604.10688
+---
+
+# Model Name
+
+SCOPE-Deepseek-R1-Distill-Qwen-1.5B
+
+This model is introduced in the paper 
+[SCOPE: Signal-Calibrated On-Policy Distillation Enhancement with Dual-Path Adaptive Weighting](https://arxiv.org/abs/2604.10688) 
+and is developed by the **Longcat Interaction Team**.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Longcat Interaction Team
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** Deepseek-R1-Distill-Qwen-1.5B
+- **Paper:** [arxiv.org/abs/2604.10688](https://arxiv.org/abs/2604.10688)
+
+### Model Sources
+
+- **Repository:** [https://github.com/machine981/SCOPE](https://github.com/machine981/SCOPE)
+- **Paper:** [https://arxiv.org/abs/2604.10688](https://arxiv.org/abs/2604.10688)
+
+## Uses
+
+### Direct Use
+
+This model can be used directly for text generation (like MATH reasoning) without any additional fine-tuning.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM  # adjust as needed
+
+tokenizer = AutoTokenizer.from_pretrained("Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B")
+model = AutoModelForCausalLM.from_pretrained("Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B")
+
+inputs = tokenizer("Your input text here", return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))