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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 3·5 min read
Post-Training Ternarization of Qwen3-4B Capability, Effective Bit Budget, Storage Compression, and Deployment
TL;DR
Post-training ternarization of the Qwen3-4B model achieves a reduction in storage size while maintaining some task performance, but accuracy drops significantly in certain areas.
✦ Why It Matters
Consider implementing post-training ternarization to optimize model storage while assessing potential accuracy trade-offs.
Key Takeaways
- Explore post-training ternarization for model compression.
- Evaluate task-specific performance impacts before deployment.
- Monitor perplexity changes to assess language model quality.
- Test packed models for inference speed against benchmarks.
How It Works
The conversion process employs KOTMS rotation and E2M-ATQ ternarization to reduce the bit representation of model weights. This allows for a significant reduction in storage size while attempting to retain model performance.
The effective bit accounting method provides a clearer understanding of the model's capabilities post-conversion.
⚠ The Catch
Despite the size reduction, the model experiences a notable drop in accuracy, particularly on certain tasks, which may limit its practical application.