This content originally appeared on HackerNoon and was authored by Reinforcement Technology Advancements
Table of Links
3 Model and 3.1 Associative memories
6 Empirical Results and 6.1 Empirical evaluation of the radius
6.3 Training Vanilla Transformers
7 Conclusion and Acknowledgments
Appendix B. Some Properties of the Energy Functions
Appendix C. Deferred Proofs from Section 5
Appendix D. Transformer Details: Using GPT-2 as an Example
Appendix D. Transformer Details: Using GPT-2 as an Example
\ Another commonly used loss is the perplexity, which is equivalent to the exponentiated version of the cross-entropy.
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:::info Authors:
(1) Xueyan Niu, Theory Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd.;
(2) Bo Bai baibo (8@huawei.com);
(3) Lei Deng (deng.lei2@huawei.com);
(4) Wei Han (harvey.hanwei@huawei.com).
:::
:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
:::
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This content originally appeared on HackerNoon and was authored by Reinforcement Technology Advancements
Reinforcement Technology Advancements | Sciencx (2025-06-24T03:00:07+00:00) GPT-2 Architecture and Training Details: Parameters & Cross-Entropy Loss. Retrieved from https://www.scien.cx/2025/06/24/gpt-2-architecture-and-training-details-parameters-cross-entropy-loss/
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