Scavenging Hyena: Distilling Transformers into Long Convolution Models
The rapid evolution of Large Language Models (LLMs), epitomized by
architectures like GPT-4, has reshaped the landscape of natural language
processing. This paper introduces a pioneering approach to address the
efficiency concerns associated with LLM pre-training, proposing the use of
knowledge distillation for cross-architecture transfer. Leveraging insights
from the efficient Hyena mechanism, our method replaces attention heads in
transformer models by Hyena, offering a cost-effective alternative to
traditional pre-training while confronting the challenge of processing long
contextual information, inherent in quadratic attention mechanisms. Unlike
conventional compression-focused methods, our technique not only enhances
inference speed but also surpasses pre-training in terms of both accuracy and
efficiency. In the era of evolving LLMs, our work contributes to the pursuit of
sustainable AI solutions, striking a balance between computational power and
environmental impact.
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Tokiniaina Raharison Ralambomihanta
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2024-02-02T09:01:33.110Z
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Toky Raharison Ralambomihanta
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JustToky
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Shahrad Mohammadzadeh
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2024-02-02T09:01:40.347Z
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MZ
user
Shahradmz
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Mohammad Sami Nur Islam
65bb04827677ff48f5c4be70
Wassim Jabbour
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2024-02-02T09:01:35.222Z
65bc0291b7db0ab095d59735
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Wassim Jabbour
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WassimJabz
65bb04827677ff48f5c4be71
Laurence Liang
Scavenging Hyena: Distilling Transformers into Long Convolution Models
The rapid evolution of Large Language Models (LLMs), epitomized by
architectures like GPT-4, has reshaped the landscape of natural language
processing. This paper introduces a pioneering approach to address the
efficiency concerns associated with LLM pre-training, proposing the use of
knowledge distillation for cross-architecture transfer. Leveraging insights
from the efficient Hyena mechanism, our method replaces attention heads in
transformer models by Hyena, offering a cost-effective alternative to
traditional pre-training while confronting the challenge of processing long
contextual information, inherent in quadratic attention mechanisms. Unlike
conventional compression-focused methods, our technique not only enhances
inference speed but also surpasses pre-training in terms of both accuracy and
efficiency. In the era of evolving LLMs, our work contributes to the pursuit of
sustainable AI solutions, striking a balance between computational power and
environmental impact.
Knowledge distillation using the Hyena mechanism enhances both accuracy and efficiency in LLM pre-training, replacing attention heads and addressing long contextual information processing.
Large Language Models LLM GPT-4 knowledge distillation Hyena mechanism transformer models attention heads computational power environmental impact