The Transformer Algorithm with the Lowest Optimal Time Complexity Possible

Prologue We are only talking about time complexity in this article - deliberately. For space complexity, refer to my article on 1-bit transformers, available here: https://hackernoon.com/why-1-bit-transformers-will-change-the-world?embedable=true Introduction We are racing forward into the future as far as Generative AI technology is concerned and the algorithms behind Large language Models are no exception.

Pre-training of Deep Bidirectional Transformers for Language Understanding.GPT-3: Language Models are Few-Shot LearnersBrown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. . GPT-3: Language Models are Few-Shot Learners.RoBERTa: A Robustly Optimized BERT Pretraining ApproachLiu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. . RoBERTa: A Robustly Optimized BERT Pretraining Approach.

smaller, faster, cheaper and lighter. For the Prologue and Epilogue For quantization, this paper is definitely worth a read: Research Paper - BitNet: Scaling 1-bit Transformers for Large Language Models: From the Abstract*: Experimental results on language modeling show that* BitNet achieves competitive performance while substantially reducing memory footprint and energy consumption compared to state-of-the-art 8-bit quantization methods and FP16 Transformer baselines.

Pre-training of Deep Bidirectional Transformers for Language Understanding. GPT-3: Language Models are Few-Shot LearnersBrown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. . GPT-3: Language Models are Few-Shot Learners. RoBERTa: A Robustly Optimized BERT Pretraining ApproachLiu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. . RoBERTa: A Robustly Optimized BERT Pretraining Approach.

smaller, faster, cheaper and lighter. Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Dai, Z., Yang, Z., Yang, Y., Carbonell, J., & Le, Q. V. . Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context . Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Longformer: The Long-Document Transformer Beltagy, I., Peters, M. E., & Cohan, A. .

Hessian Based Ultra Low Precision Quantization of BERT Pre-training of Deep Bidirectional Transformers for Language Understanding GPT-3: Language Models are Few-Shot Learners Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. . GPT-3: Language Models are Few-Shot Learners . GPT-3: Language Models are Few-Shot Learners GPT-3: Language Models are Few-Shot Learners RoBERTa: A Robustly Optimized BERT Pretraining Approach Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. .

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