Introduces BrainSurgery, a declarative YAML-based tool for reproducible tensor surgery on deep learning checkpoints with built-in validation assertions, shown via examples and case studies in model upcycling and LoRA extraction.
Interpreto: An Explainability Library for Transformers
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abstract
Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs. It provides two complementary families of methods: attribution methods and concept-based explanations. The library bridges recent research and practical tooling by exposing explanation workflows through a unified API for both classification and text generation. A key differentiator is its end-to-end concept-based pipeline (from activation extraction to concept learning, interpretation, and scoring), which goes beyond feature-level attributions and is uncommon in existing libraries. See GitHub: https://github.com/FOR-sight-ai/interpreto and the demo website: https://for-sight-ai.github.io/interpreto-demo/.
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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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BrainSurgery: Reproducible and Reliable Declarative Weight Manipulations for Model Editing and Upcycling
Introduces BrainSurgery, a declarative YAML-based tool for reproducible tensor surgery on deep learning checkpoints with built-in validation assertions, shown via examples and case studies in model upcycling and LoRA extraction.