Post-training ternarization of Qwen3-4B: effective bit budget, storage compression, and deployment
Read the original at arxiv.org→arXiv:2609.01962v1 Announce Type: new Abstract: Ultra-low-bit language models can reduce storage and memory bandwidth, but a nominal "1.58-bit" label does not fully describe the stored representation, retained...
Original headline: "Post-Training Ternarization of Qwen3-4B Capability, Effective Bit Budget, Storage Compression, and Deployment"
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- Sep 3, 04:00 UTC arXiv cs.AI lead source Post-Training Ternarization of Qwen3-4B Capability, Effective Bit Budget, Storage Compression, and Deployment