Update README.md

README.md

@@ -1,6 +1,5 @@
 ---
 title: Math-MCQ-Generator-v1
-emoji: 🧮
 colorFrom: blue
 colorTo: purple
 sdk: gradio
@@ -19,11 +18,11 @@ tags:
 
 # Math-MCQ-Generator-v1
 
-## 📋 Model Description
+## Model Description
 
 This is a fine-tuned version of `deepseek-ai/deepseek-math-7b-instruct` specialized for generating high-quality mathematics multiple choice questions (MCQs). The model has been trained using QLoRA (Quantized Low-Rank Adaptation) to efficiently adapt the base model for educational content generation.
 
-## 🎯 Capabilities
+## Capabilities
 
 - **Subject**: Mathematics
 - **Question Types**: Multiple Choice Questions (MCQs)
@@ -31,7 +30,7 @@ This is a fine-tuned version of `deepseek-ai/deepseek-math-7b-instruct` speciali
 - **Difficulty Levels**: Easy, Medium, Hard
 - **Cognitive Skills**: Recall, Direct Application, Pattern Recognition, Strategic Reasoning, Trap Aware
 
-## 📊 Training Information
+## Training Information
 
 - **Base Model**: `deepseek-ai/deepseek-math-7b-instruct`
 - **Training Method**: QLoRA (4-bit quantization)
@@ -40,10 +39,7 @@ This is a fine-tuned version of `deepseek-ai/deepseek-math-7b-instruct` speciali
 - **Final Loss**: ~0.20
 - **Training Date**: 2025-09-03
 
-## 🚀 Usage
-
-### Via Gradio Interface (Recommended)
-Visit the Spaces page to interact with the model through a user-friendly interface.
+## Usage
 
 ### Via Python API
 ```python
@@ -52,8 +48,8 @@ from peft import PeftModel
 
 # Load model
 base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-math-7b-instruct")
-model = PeftModel.from_pretrained(base_model, "your-username/math-mcq-generator-v1")
-tokenizer = AutoTokenizer.from_pretrained("your-username/math-mcq-generator-v1")
+model = PeftModel.from_pretrained(base_model, "danxh/math-mcq-generator-v1")
+tokenizer = AutoTokenizer.from_pretrained("danxh/math-mcq-generator-v1")
 
 # Generate MCQ
 prompt = '''### Instruction:
@@ -73,7 +69,7 @@ outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7)
 response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 ```
 
-## 📈 Performance
+## Performance
 
 The model demonstrates strong performance in generating contextually appropriate mathematics MCQs with:
 - Proper question formatting
@@ -81,16 +77,7 @@ The model demonstrates strong performance in generating contextually appropriate
 - Appropriate difficulty scaling
 - Subject-matter accuracy
 
-## 🤝 Collaboration
-
-This model is part of a collaborative effort to create specialized educational AI tools. A companion Physics MCQ generator is also available.
-
-## 📄 License
+## License
 
 MIT License - Feel free to use, modify, and distribute.
 
-## 🙏 Acknowledgments
-
-- Base model by DeepSeek AI
-- Training infrastructure supported by Hugging Face
-- Educational content expertise from domain specialists