Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference (2024.findings-eacl)
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| Challenge: | Large language models excel at understanding and generating human-like text, but their widespread deployment can be prohibitively expensive. |
| Approach: | They propose a method that makes large language models dynamic without Pre-Training . they use modularity in networks and sort sub-models based on computation/accuracy in a nested manner. |
| Outcome: | The proposed method can make large language models dynamic without pre-training and replace standard fine-tuning with sorted fine- tuning. |
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