BrainSurgery: Reproducible and Reliable Declarative Weight Manipulations for Model Editing and Upcycling

Hugging Face Daily Papers Papers
model-editing upcycling weight-manipulation tensor-surgery yaml-plans open-source tool

Summary

BrainSurgery is a tool for reproducible and declarative weight manipulations on neural network checkpoints, enabling model editing and upcycling through YAML plans with built-in validation.

As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alter model weights for layer restructuring, precision casting, low-rank factorization, and architectural debugging, yet these workflows often rely on fragile ad-hoc Python scripts. Here, we introduce BrainSurgery, a tool for robust and reproducible "tensor surgery" on neural network checkpoints, and provide a system demonstration covering four examples and three case studies from model upcycling to LoRA extraction. By abstracting storage formats and memory management, BrainSurgery executes complex transformations through declarative YAML plans. It supports structural modifications, mathematical transformations, and tensor reshaping through expressive regex and structural targeting, while built-in assertions validate tensor shapes, data types, and values to prevent silent errors. We envision that BrainSurgery will provide a strong foundation for future research through its reproducible and validated operations.
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Source: https://huggingface.co/papers/2606.09707

Abstract

BrainSurgery is a tool for robust and reproducible tensor manipulation of neural network checkpoints through declarative YAML plans with built-in validation.

As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alter model weights for layer restructuring, precision casting, low-rank factorization, and architectural debugging, yet these workflows often rely on fragile ad-hoc Python scripts. Here, we introduce BrainSurgery, a tool for robust and reproducible “tensor surgery” onneural network checkpoints, and provide a system demonstration covering four examples and three case studies from model upcycling to LoRA extraction. By abstracting storage formats and memory management, BrainSurgery executes complex transformations throughdeclarative YAML plans. It supportsstructural modifications,mathematical transformations, andtensor reshapingthrough expressiveregexandstructural targeting, while built-inassertionsvalidatetensor shapes,data types, andvaluesto prevent silent errors. We envision that BrainSurgery will provide a strong foundation for future research through its reproducible and validated operations.

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