EdgeRazor: Mixed-Precision Distillation Framework for 1.88-bit LLMs
ttkciar · reddit · 2026-08-03
EdgeRazor is a lightweight framework for LLMs that uses mixed-precision quantization-aware distillation to preserve model competence under extremely low memory conditions.
- Core Mechanism: Employs an entropy-guided distillation process to translate the teacher model's logit probability distributions into the student model's low-bit/mixed-precision features without preserving original parameter structures.
- Performance: While computationally more expensive than standard quantization, it is significantly cheaper than Quantization-Aware Training (QAT) and maintains robust reasoning even at 1.88 bits per parameter.
- Engineering Friendly: Because it doesn't alter the internal representation, inference engines like llama.cpp can run it without code modifications.
The authors have open-sourced the code and uploaded demo models based on MobileLLM and Qwen3 to Hugging Face.
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