Create README.md

README.md

@@ -0,0 +1,122 @@
+---
+license: mit
+datasets:
+- xl-zhao/PromptCoT-Problem-Generation-Dataset
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B
+---
+# **PromptCoT: Synthesizing Olympiad-Level Problems for Mathematical Reasoning in Large Language Modelsg**  
+
+[![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 Problem Generation Model** is a lightweight yet powerful model for synthesizing high-quality Olympiad-level mathematical problems. It enables the scalable construction of problem sets to facilitate post-training tasks such as **Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL)**. By systematically modeling expert problem design, PromptCoT helps generate logically consistent and intellectually demanding problems at scale.
+
+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 the model for **direct inference** using Hugging Face’s `generate` API:  
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "xl-zhao/PromptCoT-Problem-Generation-Model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")
+
+foundational_concepts = [
+    "Ability to apply quantitative reasoning and estimation techniques to solve problems, including making approximations and using logical deductions to arrive at a solution.",
+    "Ability to solve equations involving complex numbers, including finding conditions under which two complex numbers are equal, particularly in the context of their magnitudes and arguments.",
+    "Fractional arithmetic: Performing calculations with fractions to determine the final probability.",
+    "Interpreting and solving problems involving nested operations or functions.",
+    "Using logical reasoning to connect given data points and derive conclusions."
+]
+
+difficulty_level = "HMMT-Feb"
+
+prompt = (
+    "Given foundational concepts and difficulty level, identify connections and develop a question "
+    "that integrates these concepts with appropriate complexity.\n\n"
+    "Foundational Concepts:\n"
+    + "\n".join(f"{i+1}. {concept}" for i, concept in enumerate(foundational_concepts))
+    + f"\n\nDifficulty Level: {difficulty_level}"
+)
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    output = model.generate(**inputs, max_length=4096, temperature=0.6)
+
+generated_problem = tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_problem)
+```
+
+---
+
+## ⚡ **Using vLLM for Fast Inference**  
+For optimized inference, use `vLLM`:  
+```python
+from vllm import LLM, SamplingParams
+
+model_name = "xl-zhao/PromptCoT-Problem-Generation-Model"
+llm = LLM(model=model_name, tensor_parallel_size=1)
+
+foundational_concepts = [
+    "Ability to apply quantitative reasoning and estimation techniques to solve problems, including making approximations and using logical deductions to arrive at a solution.",
+    "Ability to solve equations involving complex numbers, including finding conditions under which two complex numbers are equal, particularly in the context of their magnitudes and arguments.",
+    "Fractional arithmetic: Performing calculations with fractions to determine the final probability.",
+    "Interpreting and solving problems involving nested operations or functions.",
+    "Using logical reasoning to connect given data points and derive conclusions."
+]
+
+difficulty_level = "HMMT-Feb"
+
+prompt = (
+    "Given foundational concepts and difficulty level, identify connections and develop a question "
+    "that integrates these concepts with appropriate complexity.\n\n"
+    "Foundational Concepts:\n"
+    + "\n".join(f"{i+1}. {concept}" for i, concept in enumerate(foundational_concepts))
+    + f"\n\nDifficulty Level: {difficulty_level}"
+)
+
+sampling_params = SamplingParams(temperature=0.6, max_tokens=4096)
+outputs = llm.generate([prompt], sampling_params)
+
+print(outputs[0].outputs[0].text)
+```
+
+---
+
+## 🔗 **Full Usage & Advanced Options**  
+For advanced usage, including **batch inference and rejection sampling for filtering high-quality problems**, 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}
+}
+```
+
+
+