Activation Outliers in Transformer Quantization: Reproduction, Statistical Analysis, and Deployment Tradeoffs | ScienceToStartup | ScienceToStartup

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References (8)

[1]

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

2022Guangxuan Xiao, Ji Lin et al.

[2]

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

2022Elias Frantar, Saleh Ashkboos et al.

[3]

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

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"A solution for mitigating accuracy degradation in transformer quantization by focusing on structured channel dominance, designed for efficient deployment."

Transformer Optimization•Score: 6•View PDF ↗

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Activation Outliers in Transformer Quantization: Reproduction, Statistical Analysis, and Deployment Tradeoffs

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References (8)

Founder's Pitch

"A solution for mitigating accuracy degradation in transformer quantization by focusing on structured channel dominance, designed for efficient deployment."

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Activation Outliers in Transformer Quantization: Reproduction, Statistical Analysis, and Deployment Tradeoffs

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References (8)

Founder's Pitch

"A solution for mitigating accuracy degradation in transformer quantization by focusing on structured channel dominance, designed for efficient deployment."

Commercial Viability Breakdown

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