title: NNsight and NDIF: Democratizing Access to Foundation Model Internals
summary: The enormous scale of state-of-the-art foundation models has limited their
  accessibility to scientists, because customized experiments at large model
  sizes require costly hardware and complex engineering that is impractical for
  most researchers. To alleviate these problems, we introduce NNsight, an
  open-source Python package with a simple, flexible API that can express
  interventions on any PyTorch model by building computation graphs. We also
  introduce NDIF, a collaborative research platform providing researchers access
  to foundation-scale LLMs via the NNsight API. Code, documentation, and
  tutorials are available at https://www.nnsight.net.
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paper_title: NNsight and NDIF: Democratizing Access to Foundation Model Internals
paper_summary: The enormous scale of state-of-the-art foundation models has limited their
  accessibility to scientists, because customized experiments at large model
  sizes require costly hardware and complex engineering that is impractical for
  most researchers. To alleviate these problems, we introduce NNsight, an
  open-source Python package with a simple, flexible API that can express
  interventions on any PyTorch model by building computation graphs. We also
  introduce NDIF, a collaborative research platform providing researchers access
  to foundation-scale LLMs via the NNsight API. Code, documentation, and
  tutorials are available at https://www.nnsight.net.
paper_ai_keywords: neural graphs computation graphs PyTorch model open-source Python package foundation-scale LLMs collaborative research platform NNsight NDIF