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README.md

@@ -5,7 +5,6 @@ tags:
 - math
 - optimized
 - wanda
-- activation-pruning
 base_model: Qwen/Qwen2.5-3B-Instruct
 pipeline_tag: text-generation
 ---
@@ -14,35 +13,28 @@ pipeline_tag: text-generation
 
 > 🎯 **MATH-optimized** | 📦 **Safe** pruning | ⚡ **1% weights pruned**
 
-This model is a **conservatively pruned** version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct), specialized for **MATH** tasks using activation-aware weight pruning (Wanda-style).
+This model is a **conservatively pruned** version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
 
-## ✨ Key Features
-
-- **Specialization**: Optimized for Math tasks
-- **Pruning Method**: Wanda-style (|W| × |activation|) importance scoring
-- **Size Reduction**: 1% weights pruned
-- **Use Case**: High accuracy retention, ideal for production use
-
-## 📊 Performance Comparison
+## Performance Comparison
 
 | Category | Original | Pruned | Change |
 |----------|----------|--------|--------|
-| Python | 100.0% | 100.0% | → |
-| Html | 6.7% | 6.7% | → |
-| Trivia | 66.7% | 66.7% | → |
-| **Math** | 60.0% | 60.0% ⭐ | → |
-| Reasoning | 100.0% | 100.0% | → |
-| Medical | 86.7% | 86.7% | → |
-| Linux | 100.0% | 100.0% | → |
-| Writing | 73.3% | 73.3% | → |
+| Python | 92.3% | 92.3% | → |
+| Html | 40.0% | 40.0% | → |
+| Trivia | 100.0% | 100.0% | → |
+| **Math** | 100.0% | 100.0% ⭐ | → |
+| Reasoning | 91.7% | 91.7% | → |
+| Medical | 64.3% | 64.3% | → |
+| Linux | 69.2% | 69.2% | → |
+| Writing | 54.5% | 54.5% | → |
 
-**Average**: 74.2% → 74.2% (+0.0%)
+**Average**: 76.5% → 76.5% (+0.0%)
 
-**Math Retention**: 100.0% of original performance
+**Math Retention**: 100.0%
 
 ![Comparison Graph](comparison_graph.png)
 
-## 🚀 Quick Start
+## Quick Start
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -50,31 +42,20 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 model = AutoModelForCausalLM.from_pretrained("CompactAI/Qwen2.5-3B-Instruct-math-safe")
 tokenizer = AutoTokenizer.from_pretrained("CompactAI/Qwen2.5-3B-Instruct-math-safe")
 
-# Example usage
 inputs = tokenizer("Your prompt here", return_tensors="pt")
 outputs = model.generate(**inputs, max_new_tokens=100)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
-## 📋 Technical Details
+## Technical Details
 
 | Property | Value |
 |----------|-------|
 | Base Model | [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) |
 | Specialization | Math |
 | Prune Mode | Safe |
-| Pruning Method | Activation-based weight pruning (Wanda) |
 | Weight Reduction | 1% weights pruned |
 
-## 🔗 Related Models
-
-This model is part of the **Qwen2.5-3B-Instruct** pruned model collection. Variants:
-- **Safe** - Conservative pruning (~10-20%), high accuracy retention
-- **Aggressive** - Maximum compression (~40-50%), best for edge deployment
+## License
 
-## 📜 License
-
-This model inherits the license from the base model [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
-
----
-*Generated by ZANNPS [Zeto Automatic Neural Network Pruning System]*
+This model inherits the license from the base model.

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