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  # Emo-v1
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- <div align="center">
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-
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- ![Emo-Qwen](https://img.shields.io/badge/Model-Emo--Qwen2.5--3B-blue?style=for-the-badge&logo=huggingface)
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- ![Reasoning](https://img.shields.io/badge/Task-Math_Reasoning-red?style=for-the-badge)
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- ![License](https://img.shields.io/badge/License-Apache_2.0-green?style=for-the-badge)
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- **A lightweight 3B parameter model fine-tuned to "Think like O1".**
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  *Specialized in Algebra, Logic Puzzles, and Step-by-Step Reasoning.*
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  </div>
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  ## 📖 Model Description
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- **Emo-Qwen2.5-3B** is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct), optimized for mathematical reasoning and logic.
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  Unlike standard chat models that often guess answers, Emo-Qwen is trained to **decompose problems into explicit steps** before providing a final solution. It mimics the "Chain of Thought" (CoT) process found in larger reasoning models (like OpenAI's o1), making it surprisingly capable for its small size.
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- ### 🚀 Key Features
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  * **Step-by-Step Reasoning:** Forces a "Let's break this down" approach to minimize logic errors.
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  * **Math Specialist:** Trained on the `nvidia/OpenMathInstruct-2` dataset, covering algebra, calculus, and probability.
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  * **LaTeX Support:** Optimized to output mathematical formulas in clean LaTeX format (e.g., $x^2 + y^2$).
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  * **Efficient:** At only 3 Billion parameters, it runs on consumer hardware (even free Kaggle/Colab T4 GPUs) with low latency.
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- ## 💻 How to Use
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-
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- ### System Prompt (Crucial)
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- To trigger the reasoning capability, you **must** use the specific system prompt below:
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- > **"You are a helpful math assistant. Think step by step. IMPORTANT: Use LaTeX formatting for all math."**
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  ### Python Inference Code
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  # Emo-v1
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+ **A lightweight 3B parameter model fine-tuned for Reasoning.**
 
 
 
 
 
 
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  *Specialized in Algebra, Logic Puzzles, and Step-by-Step Reasoning.*
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  </div>
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  ## 📖 Model Description
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+ **Emo-v1** is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct), optimized for mathematical reasoning and logic.
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  Unlike standard chat models that often guess answers, Emo-Qwen is trained to **decompose problems into explicit steps** before providing a final solution. It mimics the "Chain of Thought" (CoT) process found in larger reasoning models (like OpenAI's o1), making it surprisingly capable for its small size.
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+ ### Key Features
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  * **Step-by-Step Reasoning:** Forces a "Let's break this down" approach to minimize logic errors.
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  * **Math Specialist:** Trained on the `nvidia/OpenMathInstruct-2` dataset, covering algebra, calculus, and probability.
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  * **LaTeX Support:** Optimized to output mathematical formulas in clean LaTeX format (e.g., $x^2 + y^2$).
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  * **Efficient:** At only 3 Billion parameters, it runs on consumer hardware (even free Kaggle/Colab T4 GPUs) with low latency.
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+ ## How to Use
 
 
 
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  ### Python Inference Code
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