Post-Training Ternarization of Qwen3-4B Capability, Effective Bit Budget, Storage Compression, and Deployment
cs.AI, cs.LG
Submitted: 2026-09-02
Updated: 2026-09-02
Terminology
Sources
- Attention Is All You Need
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
- SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
- The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
- CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs
- PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models
- TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization
- OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
- Attend to Your Own Thoughts: Breaking the Barrier for Post-Training Quantization of Reasoning LLMs through the Lens of 1.58-Bit Quantization
- Qwen3 Technical Report
- Capability-Stratified Degradation in Ternary Language Models
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