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# Emo-v1
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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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## 📖 Model Description
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**Emo-
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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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###
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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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##
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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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