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license: apache-2.0 |
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tags: |
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- pruned |
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- python |
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- optimized |
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- wanda |
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- activation-pruning |
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base_model: LGAI-EXAONE/EXAONE-4.0-1.2B |
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pipeline_tag: text-generation |
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--- |
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# EXAONE-4.0-1.2B-python-aggressive |
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> 🎯 **PYTHON-optimized** | 📦 **Aggressive** pruning | ⚡ **7% weights pruned** |
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This model is a **aggressively 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). |
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## ✨ Key Features |
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- **Specialization**: Optimized for Python tasks |
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- **Pruning Method**: Wanda-style (|W| × |activation|) importance scoring |
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- **Size Reduction**: 7% weights pruned |
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- **Use Case**: Maximum compression for edge deployment |
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## 📊 Performance Comparison |
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| Category | Original | Pruned | Change | |
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|----------|----------|--------|--------| |
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| **Python** | 20.0% | 20.0% ⭐ | → | |
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| Html | 6.7% | 6.7% | → | |
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| Trivia | 26.7% | 53.3% | ↑ 26.7% | |
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| Math | 60.0% | 53.3% | ↓ 6.7% | |
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| Reasoning | 60.0% | 73.3% | ↑ 13.3% | |
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| Medical | 73.3% | 80.0% | ↑ 6.7% | |
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| Linux | 93.3% | 93.3% | → | |
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| Writing | 60.0% | 53.3% | ↓ 6.7% | |
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**Average**: 50.0% → 54.2% (+4.2%) |
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**Python Retention**: 100.0% of original performance |
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## 🚀 Quick Start |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("CompactAI/EXAONE-4.0-1.2B-python-aggressive") |
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tokenizer = AutoTokenizer.from_pretrained("CompactAI/EXAONE-4.0-1.2B-python-aggressive") |
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# Example usage |
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inputs = tokenizer("Your prompt here", return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=100) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## 📋 Technical Details |
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| Property | Value | |
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|----------|-------| |
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| Base Model | [LGAI-EXAONE/EXAONE-4.0-1.2B](https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-1.2B) | |
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| Specialization | Python | |
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| Prune Mode | Aggressive | |
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| Pruning Method | Activation-based weight pruning (Wanda) | |
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| Weight Reduction | 7% weights pruned | |
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## 🔗 Related Models |
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This model is part of the **EXAONE-4.0-1.2B** pruned model collection. Variants: |
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- **Safe** - Conservative pruning (~10-20%), high accuracy retention |
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- **Aggressive** - Maximum compression (~40-50%), best for edge deployment |
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## 📜 License |
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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). |
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--- |
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*Generated by ZANNPS [Zeto Automatic Neural Network Pruning System]* |
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