Mini-GPTs: Efficient Large Language Models through Contextual Pruning
In AI research, the optimization of Large Language Models (LLMs) remains a
significant challenge, crucial for advancing the field's practical applications
and sustainability. Building upon the foundational work of Professor Song Han's
lab at MIT, this paper introduces a novel approach in developing Mini-GPTs via
contextual pruning. Our methodology strategically prunes the computational
architecture of traditional LLMs, like Phi-1.5, focusing on retaining core
functionalities while drastically reducing model sizes. We employ the technique
across diverse and complex datasets, including US law, Medical Q&A, Skyrim
dialogue, English-Taiwanese translation, and Economics articles. The results
underscore the efficiency and effectiveness of contextual pruning, not merely
as a theoretical concept but as a practical tool in developing domain-specific,
resource-efficient LLMs. Contextual pruning is a promising method for building
domain-specific LLMs, and this research is a building block towards future
development with more hardware compute, refined fine-tuning, and quantization.
6583b2452031da25daaf4aca
Tim Valicenti
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2023-12-21T03:47:10.646Z
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Tim Valicenti
user
tval2
6583b2452031da25daaf4acb
Justice Vidal
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2023-12-21T03:34:29.820Z
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Justice Vidal
user
jmvidal08
6583b2452031da25daaf4acc
Ritik Patnaik
extracted_pending
2023-12-21T03:34:29.820Z
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Ritik Patnaik
user
rik01
Mini-GPTs: Efficient Large Language Models through Contextual Pruning
In AI research, the optimization of Large Language Models (LLMs) remains a
significant challenge, crucial for advancing the field's practical applications
and sustainability. Building upon the foundational work of Professor Song Han's
lab at MIT, this paper introduces a novel approach in developing Mini-GPTs via
contextual pruning. Our methodology strategically prunes the computational
architecture of traditional LLMs, like Phi-1.5, focusing on retaining core
functionalities while drastically reducing model sizes. We employ the technique
across diverse and complex datasets, including US law, Medical Q&A, Skyrim
dialogue, English-Taiwanese translation, and Economics articles. The results
underscore the efficiency and effectiveness of contextual pruning, not merely
as a theoretical concept but as a practical tool in developing domain-specific,
resource-efficient LLMs. Contextual pruning is a promising method for building
domain-specific LLMs, and this research is a building block towards future
development with more hardware compute, refined fine-tuning, and quantization.
Contextual pruning reduces the size of Large Language Models while preserving essential functionalities, enabling efficient and domain-specific deployment.
Large Language Models Mini-GPTs contextual pruning Phi-1.5 domain-specific resource-efficient hardware compute refined fine-tuning quantization