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| == En chantier ==
| | #REDIRECTION [[Peaufinage de représentations]] |
| == Définition ==
| | [[Catégorie:ENGLISH]] |
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| == Français ==
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| ''' peaufinage de représentations '''
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| ''' peaufinage d'espace latent '''
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| == Anglais ==
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| ''' representation fine-tuning '''
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| <!-- PYREFT, a representation fine-tuning (ReFT) library that supports adapting internal language model representations via trainable interventions. With fewer fine-tuning parameters and more robust performance, pyreft can boost fine-tuning efficiency, decrease fine-tuning cost, while opening the doors to study the interpretability of adapting parameters. -->
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| == Source ==
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| [https://arxiv.org/pdf/2404.03592 ''ReFT: Representation Finetuning for Language Models'', Wu et al. (2024)]
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| [https://blog.paperspace.com/reft-representation-finetuning-for-language-models/ ''ReFT: Representation Finetuning for Language Models'', Shaoni Mukherjee (2024)]
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| [https://medium.com/@techsachin/representation-fine-tuning-reft-a-powerful-parameter-efficient-way-to-fine-tune-language-models-3bc6dd14e8b5 ''Representation fine-tuning (ReFT): A Powerful Parameter-Efficient Way to Fine-tune Language Models'', Sachin Mukar]
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| [https://github.com/stanfordnlp/pyreft Github - PYREFT]
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| [[Catégorie:Publication]] | |
