HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs | ScienceToStartup | ScienceToStartup

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References (34)

[1]

Fairy2i: Training Complex LLMs from Real LLMs with All Parameters in {± 1, ± i}

2025Feiyu Wang, Xinyu Tan et al.

[2]

CAGE: Curvature-Aware Gradient Estimation For Accurate Quantization-Aware Training

2025Soroush Tabesh, Mher Safaryan et al.

[3]

LOTION: Smoothing the Optimization Landscape for Quantized Training

Founder's Pitch

"Hestia enhances deployment of large language models through efficient low-bit quantization with superior performance for memory-constrained applications."

Quantization•Score: 6•View PDF ↗

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HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs

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References (34)

Founder's Pitch

"Hestia enhances deployment of large language models through efficient low-bit quantization with superior performance for memory-constrained applications."

Commercial Viability Breakdown

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HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs

BUILDER'S SANDBOX

Build This Paper

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Startup Essentials

References (34)

Founder's Pitch

"Hestia enhances deployment of large language models through efficient low-bit quantization with superior performance for memory-constrained applications."

Commercial Viability Breakdown

Related Papers

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