Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+---
+base_model: Qwen/Qwen2.5-3B-Instruct
+library_name: transformers
+tags:
+- math
+- reasoning
+- qwen
+- fine-tuned
+- loRA
+datasets:
+- nvidia/OpenMathInstruct-2
+pipeline_tag: text-generation
+---
+
+# Emo-v1
+
+<div align="center">
+
+![Emo-Qwen](https://img.shields.io/badge/Model-Emo--Qwen2.5--3B-blue?style=for-the-badge&logo=huggingface)
+![Reasoning](https://img.shields.io/badge/Task-Math_Reasoning-red?style=for-the-badge)
+![License](https://img.shields.io/badge/License-Apache_2.0-green?style=for-the-badge)
+
+**A lightweight 3B parameter model fine-tuned to "Think like O1".**  
+*Specialized in Algebra, Logic Puzzles, and Step-by-Step Reasoning.*
+
+</div>
+
+## 📖 Model Description
+
+**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. 
+
+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.
+
+### 🚀 Key Features
+*   **Step-by-Step Reasoning:** Forces a "Let's break this down" approach to minimize logic errors.
+*   **Math Specialist:** Trained on the `nvidia/OpenMathInstruct-2` dataset, covering algebra, calculus, and probability.
+*   **LaTeX Support:** Optimized to output mathematical formulas in clean LaTeX format (e.g., $x^2 + y^2$).
+*   **Efficient:** At only 3 Billion parameters, it runs on consumer hardware (even free Kaggle/Colab T4 GPUs) with low latency.
+
+## 💻 How to Use
+
+### System Prompt (Crucial)
+To trigger the reasoning capability, you **must** use the specific system prompt below:
+
+> **"You are a helpful math assistant. Think step by step. IMPORTANT: Use LaTeX formatting for all math."**
+
+### Python Inference Code
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# 1. Load Model
+model_id = "PrimeTJ/Emo-Qwen2.5-3B-Full"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# 2. Define the Prompt
+system_prompt = "You are a helpful math assistant. Think step by step."
+user_prompt = "A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?"
+
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_prompt}
+]
+
+# 3. Generate
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=1024,
+    temperature=0.6  # Low temperature for logic
+)
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)