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