snimu/rebasin
Apply methods described in "Git Re-basin"-paper [1] to arbitrary models --- [1] Ainsworth et al. (https://arxiv.org/abs/2209.04836)
This tool helps machine learning engineers and researchers combine multiple trained neural network models, or understand the 'loss landscape' between them. You provide two or more pre-trained models, and it can 'rebasin' them to align their internal representations or interpolate smoothly between their weights. This process yields a new, potentially better-performing merged model or a sequence of interpolated models for analysis.
Available on PyPI.
Use this if you need to merge several independently trained neural networks into a single, stronger model, or if you want to analyze the 'path' between two models in terms of their performance.
Not ideal if your models are primarily transformer-based architectures, as they often don't benefit well from the permutation techniques implemented here.
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Language
Python
License
MIT
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
Dependencies
6
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