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---
license: apache-2.0
tags:
- pruned
- python
- optimized
- wanda
- activation-pruning
base_model: LGAI-EXAONE/EXAONE-4.0-1.2B
pipeline_tag: text-generation
---
# EXAONE-4.0-1.2B-python-safe
> 🎯 **PYTHON-optimized** | 📦 **Safe** pruning | ⚡ **1% weights pruned**
This model is a **conservatively pruned** version of [LGAI-EXAONE/EXAONE-4.0-1.2B](https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-1.2B), specialized for **PYTHON** tasks using activation-aware weight pruning (Wanda-style).
## ✨ Key Features
- **Specialization**: Optimized for Python 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
| Category | Original | Pruned | Change |
|----------|----------|--------|--------|
| **Python** | 20.0% | 20.0% ⭐ | → |
| Html | 6.7% | 0.0% | ↓ 6.7% |
| Trivia | 26.7% | 33.3% | ↑ 6.7% |
| Math | 60.0% | 60.0% | → |
| Reasoning | 60.0% | 60.0% | → |
| Medical | 73.3% | 73.3% | → |
| Linux | 93.3% | 93.3% | → |
| Writing | 60.0% | 60.0% | → |
**Average**: 50.0% → 50.0% (+0.0%)
**Python Retention**: 100.0% of original performance
## 🚀 Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("CompactAI/EXAONE-4.0-1.2B-python-safe")
tokenizer = AutoTokenizer.from_pretrained("CompactAI/EXAONE-4.0-1.2B-python-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
| Property | Value |
|----------|-------|
| Base Model | [LGAI-EXAONE/EXAONE-4.0-1.2B](https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-1.2B) |
| Specialization | Python |
| Prune Mode | Safe |
| Pruning Method | Activation-based weight pruning (Wanda) |
| Weight Reduction | 1% weights pruned |
## 🔗 Related Models
This model is part of the **EXAONE-4.0-1.2B** pruned model collection. Variants:
- **Safe** - Conservative pruning (~10-20%), high accuracy retention
- **Aggressive** - Maximum compression (~40-50%), best for edge deployment
## 📜 License
This model inherits the license from the base model [LGAI-EXAONE/EXAONE-4.0-1.2B](https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-1.2B).
---
*Generated by ZANNPS [Zeto Automatic Neural Network Pruning System]*
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