NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals

محفوظ في:

التفاصيل البيبلوغرافية
الحاوية / القاعدة:	arXiv.org (Dec 8, 2024), p. n/a
المؤلف الرئيسي:	Fiotto-Kaufman, Jaden
مؤلفون آخرون:	Loftus, Alexander R, Todd, Eric, Brinkmann, Jannik, Pal, Koyena, Troitskii, Dmitrii, Ripa, Michael, Belfki, Adam, Rager, Can, Juang, Caden, Mueller, Aaron, Marks, Samuel, Arnab Sen Sharma, Lucchetti, Francesca, Prakash, Nikhil, Brodley, Carla, Guha, Arjun, Bell, Jonathan, Wallace, Byron C, Bau, David
منشور في:	Cornell University Library, arXiv.org
الموضوعات:	Application programming interface Python Source code Neural networks
الوصول للمادة أونلاين:	Citation/Abstract Full text outside of ProQuest
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

الوصف
مستخلص:	We introduce NNsight and NDIF, technologies that work in tandem to enable scientific study of very large neural networks. NNsight is an open-source system that extends PyTorch to introduce deferred remote execution. NDIF is a scalable inference service that executes NNsight requests, allowing users to share GPU resources and pretrained models. These technologies are enabled by the intervention graph, an architecture developed to decouple experiment design from model runtime. Together, this framework provides transparent and efficient access to the internals of deep neural networks such as very large language models (LLMs) without imposing the cost or complexity of hosting customized models individually. We conduct a quantitative survey of the machine learning literature that reveals a growing gap in the study of the internals of large-scale AI. We demonstrate the design and use of our framework to address this gap by enabling a range of research methods on huge models. Finally, we conduct benchmarks to compare performance with previous approaches. Code documentation, and materials are available at https://nnsight.net/.
تدمد:	2331-8422
المصدر:	Engineering Database