metadata
license: apache-2.0
tags:
- pruned
- python
- optimized
- wanda
- activation-pruning
base_model: Qwen/Qwen3-0.6B
pipeline_tag: text-generation
Qwen3-0.6B-python-heavy
π― PYTHON-optimized | π¦ Heavy pruning | β‘ 15% weights pruned
This model is a aggressively pruned version of Qwen/Qwen3-0.6B, specialized for PYTHON tasks using activation-aware weight pruning (Wanda-style).
β¨ Key Features
- Specialization: Optimized for Python tasks
- Pruning Method: Wanda-style (|W| Γ |activation|) importance scoring
- Size Reduction: 15% weights pruned
- Use Case: Maximum compression while maintaining usability
π Performance Comparison
| Category | Original | Pruned | Change |
|---|---|---|---|
| Python | 40.0% | 40.0% β | β |
| Html | 0.0% | 0.0% | β |
| Trivia | 80.0% | 73.3% | β 6.7% |
| Math | 100.0% | 100.0% | β |
| Reasoning | N/A | N/A | |
| Medical | 93.3% | 93.3% | β |
| Linux | 100.0% | 100.0% | β |
| Writing | 33.3% | 33.3% | β |
Average: 63.8% β 62.9% (-1.0%)
Python Retention: 100.0% of original performance
π Quick Start
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("CompactAI/Qwen3-0.6B-python-heavy")
tokenizer = AutoTokenizer.from_pretrained("CompactAI/Qwen3-0.6B-python-heavy")
# 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))
π Technical Details
| Property | Value |
|---|---|
| Base Model | Qwen/Qwen3-0.6B |
| Specialization | Python |
| Prune Mode | Heavy |
| Pruning Method | Activation-based weight pruning (Wanda) |
| Weight Reduction | 15% weights pruned |
π Related Models
This model is part of the Qwen3-0.6B pruned model collection. Other variants:
- Extra-light (minimal pruning)
- Light
- Medium-light
- Medium
- Medium-heavy
- Heavy
- Extra-heavy (maximum compression)
π License
This model inherits the license from the base model Qwen/Qwen3-0.6B.
Generated by ZANNPS [Zeto Automatic Neural Network Pruning System]
