Update README.md

README.md

@@ -4,32 +4,113 @@ library_name: llama-cpp-python
 tags:
 - gguf
 - qwen
-- math
 - qwen2.5
+- math
+- stem
+- educational
+- reasoning
 - text-generation
-- education
 base_model: Qwen/Qwen2.5-Math-1.5B
 pipeline_tag: text-generation
+model_creator: Qwen Team (Alibaba Cloud)
+quantized_by: Md Habibur Rahman (Aasif)
 ---
 
-# Qwen 2.5 Math 1.5B (GGUF Quantized)
+# 🧮 Qwen 2.5 Math 1.5B (GGUF Quantized)
 
 This repository contains the **GGUF** quantized version of the [Qwen 2.5 Math 1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) model. 
 
-It is a specialized **Mathematical Reasoning Model** optimized for edge devices. Despite its small size (1.5B), it outperforms many larger models in mathematical problem-solving tasks.
+It is a specialized **Mathematical Reasoning Model** optimized for edge devices, offline usage, and educational apps. Despite its small size (1.5B), it outperforms many larger general-purpose models in complex mathematical problem-solving tasks.
 
-**Model Creator:** Qwen Team (Alibaba Cloud)  
 **Quantized By:** Md Habibur Rahman (Aasif)  
-**Quantization Format:** GGUF (Q4_K_M - Balanced for Math Accuracy)  
+**Quantization Format:** GGUF (Q4_K_M) - *Optimized for balance between Math Accuracy and Speed.*
+
+## 🌟 Key Features
+
+* **Math Specialist:** Specifically trained on massive mathematical datasets (Algebra, Calculus, Geometry, Logic).
+* **Chain-of-Thought (CoT):** Capable of showing step-by-step reasoning for solving problems.
+* **Edge AI Ready:** Extremely lightweight (~1 GB). Runs smoothly on Android, Raspberry Pi, and Older Laptops.
+* **Offline Capable:** Does not require an internet connection to solve problems.
 
-## 📐 Why This Model?
+## 🚀 Usage (Python)
 
-* **Math Specialist:** Specifically trained for algebra, calculus, and logical reasoning.
-* **Edge Ready:** Runs smoothly on Android, Raspberry Pi, and Laptops without internet.
-* **Small Footprint:** Requires less than 2 GB RAM.
+You can run this model using the `llama-cpp-python` library.
 
-## 🚀 Usage with Python
+### 1. Installation
 
-First, install the library:
 ```bash
-pip install llama-cpp-python huggingface_hub
+pip install llama-cpp-python huggingface_hub
+
+```
+2. Python Inference Code
+
+Here is a script to solve math problems with step-by-step logic:
+
+```
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
+
+# Download the model
+model_path = hf_hub_download(
+    repo_id="Habibur2/Qwen2.5-Math-1.5B-GGUF",
+    filename="qwen-math-1.5b-q4_k_m.gguf"
+)
+
+# Load Model
+# Set n_gpu_layers=-1 for full GPU usage (Fastest)
+# Set n_gpu_layers=0 for CPU only
+llm = Llama(
+    model_path=model_path,
+    n_ctx=2048,        # Context Window
+    n_threads=4,       # CPU Threads
+    n_gpu_layers=-1    # GPU Acceleration
+)
+
+# Define a Math Problem
+math_problem = "Find the integral of x^2 + 5x with respect to x."
+
+# System Prompt is Crucial for Math Models
+messages = [
+    {"role": "system", "content": "You are a helpful mathematical assistant. Please solve the problem step-by-step and show your reasoning clearly."},
+    {"role": "user", "content": math_problem}
+]
+
+# Generate Solution
+output = llm.create_chat_completion(
+    messages=messages,
+    max_tokens=1024,   # Math solutions need more tokens
+    temperature=0.1    # Low temperature (0.1) is best for precise math
+)
+
+print("🤖 Solution:\n")
+print(output['choices'][0]['message']['content'])
+
+```
+⚙️ Technical Specifications
+
+Feature,Details
+Original Model,Qwen 2.5 Math 1.5B Instruct
+Architecture,"Transformer (RoPE, SwiGLU)"
+Parameters,1.5 Billion
+Quantization Type,Q4_K_M (4-bit Medium)
+File Size,~1.12 GB
+Recommended RAM,2 GB+
+
+
+🧪 Benchmark & Capabilities
+
+This model excels at:
+
+    Algebra & Arithmetic: Solving equations, inequalities, and basic operations.
+
+    Calculus: Differentiation and Integration problems.
+
+    Word Problems: Understanding and translating text into mathematical equations.
+
+    LaTeX Output: Can generate answers in LaTeX format for academic rendering.
+
+👨‍💻 About the Project
+
+This model was quantized and uploaded by Md Habibur Rahman as part of a research initiative on Offline Edge AI & Small Language Models (SLM). The goal is to democratize access to powerful educational AI tools without relying on heavy cloud infrastructure.
+
+Disclaimer: While this model is highly capable, always verify complex mathematical solutions.