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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 10·5 min read
Scaling Post-Training Ternarisation to Qwen3-8B Capability Retention, Reproduction, Lossless Packing, and Packed Execution
TL;DR
A new post-training conversion pipeline for the Qwen3-8B model enhances capability retention and execution efficiency, achieving a perplexity ratio of 1.361x across multiple datasets.
✦ Why It Matters
Consider implementing aggressive post-training techniques to enhance model efficiency and performance in your projects.
Key Takeaways
- Explore KOTMS rotation and E2M-ATQ for model optimization.
- Utilize the 8B model for improved perplexity in language tasks.
- Implement lossless packing techniques to reduce storage needs.
- Investigate kernel optimization for faster execution speeds.
How It Works
The conversion pipeline employs KOTMS rotation to adjust model weights, E2M-ATQ adaptive ternarisation to optimize bit representation, and GPTQ-style error compensation to mitigate performance loss during the conversion process. This combination allows for effective scaling while maintaining the model's capabilities.