TWIG stores neural weights as shared paths of discrete decisions
On five LLM weight matrices, a flat power-of-two TWIG needs 9-15% fewer additions than GPTQ at equal error.
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Stories tagged #gptq
G^2PTQ: Improving LLM Post-Training Quantization with Generalized Gradient Compensation
G2PTQ refreshes gradients and Hessians during LLM quantization, beating GPTQ-style baselines.
Softmax Reparameterization for Output-Head Quantization
A post-training softmax reparameterization cuts output-head quantization error, dropping Phi-4-mini AW-MSE KL from 0.936 to 0.256.
GGUF vs GPTQ vs AWQ vs EXL2: LLM Model Formats Explained (2026)
An explainer separates LLM storage containers (safetensors, GGUF) from quantization methods (GPTQ, AWQ, EXL2/EXL3), with memory math and quality trade-offs.