The potential of speeding up transformer inference lies in identifying where task recognition occurs in the model, which helps in optimizing processing and reducing redundancy.
By strategically removing context-tokens processing after a certain layer in a model like LLAMA7B, we can achieve significant inference speedups with minimal impact on performance.
Results show that after processing 14 layers, a 45% savings can be obtained with a prompt size of 5, indicating substantial efficiency gains.
For instruction-tuned models, even in the absence of examples, there's potential for significant time and memory savings due to long-form instructions used for controlling model behavior.
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