GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection | ScienceToStartup | ScienceToStartup

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References (27)

[1]

Data Shapley in One Training Run

2024Jiachen T. Wang, Prateek Mittal et al.

[2]

LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding

2024Mostafa Elhoushi, Akshat Shrivastava et al.

[3]

LESS: Selecting Influential Data for Targeted Instruction Tuning

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"GAST is a novel method for parameter-efficient fine-tuning of large language models that optimally selects impactful data points and layers."

LLM Training•Score: 4•View PDF ↗

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GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection

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References (27)

Founder's Pitch

"GAST is a novel method for parameter-efficient fine-tuning of large language models that optimally selects impactful data points and layers."

Commercial Viability Breakdown

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GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection

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References (27)

Founder's Pitch

"GAST is a novel method for parameter-efficient fine-tuning of large language models that optimally selects impactful data points and layers."

Commercial Viability Breakdown

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