LORA-CRAFT: Cross-layer Rank Adaptation via Frozen Tucker Decomposition of Pre-trained Attention Weights | ScienceToStartup | ScienceToStartup

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References (12)

[1]

SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules

2024Xiangyu Chen, Jing Liu et al.

[2]

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

2024Jiawei Zhao, Zhenyu (Allen) Zhang et al.

[3]

FLoRA: Low-Rank Core Space for N-dimension

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"Develop parameter-efficient adaptation methods for transformers using frozen Tucker decomposition in CRAFT."

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LORA-CRAFT: Cross-layer Rank Adaptation via Frozen Tucker Decomposition of Pre-trained Attention Weights

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"Develop parameter-efficient adaptation methods for transformers using frozen Tucker decomposition in CRAFT."

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LORA-CRAFT: Cross-layer Rank Adaptation via Frozen Tucker Decomposition of Pre-trained Attention Weights

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Founder's Pitch

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