Create README.md

README.md

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+---
+license: mit
+datasets:
+- xl-zhao/PromptCoT-DS-Dataset
+language:
+- en
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+---
+# **PromptCoT: Synthesizing Olympiad-Level Problems for Mathematical Reasoning in Large Language Models** 
+
+[![ArXiv](https://img.shields.io/badge/arXiv-2503.02324-red)](http://arxiv.org/abs/2503.02324)  
+[![GitHub](https://img.shields.io/badge/GitHub-PromptCoT-blue)](https://github.com/zhaoxlpku/PromptCoT)  
+
+---
+
+## 🚀 **Overview**  
+The **PromptCoT-DS** series models are distilled mathematical reasoning models trained using **more challenging problem sets generated by the PromptCoT pipeline**. These models are derived from **DeepSeek-R1-Distill-Qwen** and benefit from an enhanced training dataset designed to improve mathematical reasoning capabilities.  
+
+✔ **PromptCoT-DS-1.5B** → Distilled from **DeepSeek-R1-Distill-Qwen-7B** (1.5B parameters)  
+✔ **PromptCoT-DS-7B** → Distilled from **DeepSeek-R1-Distill-Qwen-7B** (7B parameters)  
+
+For more details, refer to our **paper on ArXiv**: [🔗 PromptCoT: Synthesizing Olympiad-Level Problems for Mathematical Reasoning in Large Language Models](http://arxiv.org/abs/2503.02324).  
+
+---
+
+
+## 🔥 **Quick Start: Using the Model**  
+
+### **1️⃣ Install Dependencies**  
+```bash
+pip install transformers vllm torch accelerate
+```
+
+### **2️⃣ Load the Model with Hugging Face Transformers**  
+You can use **PromptCoT-DS** models to solve **mathematical problems** using Hugging Face’s `generate` API:  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "xl-zhao/PromptCoT-DS-1.5B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")
+
+problem_statement = (
+    "A robe takes 2 bolts of blue fiber and half that much white fiber.  How many bolts in total does it take?"
+)
+
+prompt = (
+    "<|begin▁of▁sentence|>Please reason step by step, and put your final answer within \\boxed{{}}."
+    "<|User|>" + problem_statement + "<|Assistant|>"
+)
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    output = model.generate(**inputs, max_length=32768, temperature=0.6)
+
+generated_solution = tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_solution)
+```
+
+---
+
+## ⚡ **Using vLLM for Fast Inference**  
+For optimized inference, use `vLLM`:  
+```python
+from vllm import LLM, SamplingParams
+
+model_name = "xl-zhao/PromptCoT-DS-1.5B"
+llm = LLM(model=model_name, tensor_parallel_size=1)
+
+problem_statement = (
+    "A robe takes 2 bolts of blue fiber and half that much white fiber.  How many bolts in total does it take?"
+)
+
+prompt = (
+    "<|begin▁of▁sentence|>Please reason step by step, and put your final answer within \\boxed{{}}."
+    "<|User|>" + problem_statement + "<|Assistant|>"
+)
+
+sampling_params = SamplingParams(temperature=0.6, max_tokens=32768)
+outputs = llm.generate([prompt], sampling_params)
+
+print(outputs[0].outputs[0].text)
+```
+
+---
+
+## 🔗 **Full Usage & Advanced Options**  
+For advanced usage, including batch inference and evaluation on mathematical benchmarks, refer to the **full repository on GitHub**:  
+🔹 [GitHub: PromptCoT](https://github.com/zhaoxlpku/PromptCoT)  
+
+---
+
+## 📜 **Citation**  
+If you use **PromptCoT**, please consider citing:  
+```
+@article{zhao2025promptcot,
+  author    = {Zhao, Xueliang and Wu, Wei and Guan, Jian and Kong, Lingpeng},
+  title     = {PromptCoT: Synthesizing Olympiad-Level Problems for Mathematical Reasoning in Large Language Models},
+  year      = {2025},
+  journal   = {arXiv preprint arXiv:2503.02324},
+  url       = {http://arxiv.org/abs/2503.02324}
+}
+```