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README.md

@@ -5,44 +5,36 @@ tags:
 - python
 - optimized
 - wanda
-- activation-pruning
 base_model: Qwen/Qwen2.5-3B-Instruct
 pipeline_tag: text-generation
 ---
 
 # Qwen2.5-3B-Instruct-python-aggressive
 
-> 🎯 **PYTHON-optimized** | 📦 **Aggressive** pruning | ⚡ **20% weights pruned**
+> 🎯 **PYTHON-optimized** | 📦 **Aggressive** pruning | ⚡ **35% weights pruned**
 
-This model is a **aggressively pruned** version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct), specialized for **PYTHON** tasks using activation-aware weight pruning (Wanda-style).
+This model is a **aggressively pruned** version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
 
-## ✨ Key Features
-
-- **Specialization**: Optimized for Python tasks
-- **Pruning Method**: Wanda-style (|W| × |activation|) importance scoring
-- **Size Reduction**: 20% weights pruned
-- **Use Case**: Maximum compression for edge deployment
-
-## 📊 Performance Comparison
+## Performance Comparison
 
 | Category | Original | Pruned | Change |
 |----------|----------|--------|--------|
-| **Python** | 40.0% | 13.3% ⭐ | ↓ 26.7% |
-| Html | 6.7% | 0.0% | ↓ 6.7% |
-| Trivia | 88.9% | 73.3% | ↓ 15.6% |
-| Math | 57.8% | 62.2% | ↑ 4.4% |
-| Reasoning | 33.3% | 28.9% | ↓ 4.4% |
-| Medical | 93.3% | 84.4% | ↓ 8.9% |
-| Linux | 95.6% | 93.3% | ↓ 2.2% |
-| Writing | 62.2% | 60.0% | ↓ 2.2% |
+| **Python** | 92.3% | 84.6% ⭐ | ↓ 7.7% |
+| Html | 40.0% | 30.0% | ↓ 10.0% |
+| Trivia | 100.0% | 86.7% | ↓ 13.3% |
+| Math | 100.0% | 100.0% | → |
+| Reasoning | 91.7% | 83.3% | ↓ 8.3% |
+| Medical | 64.3% | 35.7% | ↓ 28.6% |
+| Linux | 69.2% | 61.5% | ↓ 7.7% |
+| Writing | 54.5% | 36.4% | ↓ 18.2% |
 
-**Average**: 59.7% → 51.9% (-7.8%)
+**Average**: 76.5% → 64.8% (-11.7%)
 
-**Python Retention**: 33.3% of original performance
+**Python Retention**: 91.7%
 
 ![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-python-aggressive")
 tokenizer = AutoTokenizer.from_pretrained("CompactAI/Qwen2.5-3B-Instruct-python-aggressive")
 
-# 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 | Python |
 | Prune Mode | Aggressive |
-| Pruning Method | Activation-based weight pruning (Wanda) |
-| Weight Reduction | 20% weights pruned |
-
-## 🔗 Related Models
+| Weight Reduction | 35% weights pruned |
 
-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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