Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights

Jonathan Kahana, Or Nathan, Eliahu Horwitz, Yedid Hoshen

Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights: 36 upvotes on Hugging Face Daily Papers, #6 of 21 papers on 2025-02-14. Day-by-day upvote history.

With the increasing numbers of publicly available models, there are probably pretrained, online models for most tasks users require. However, current model search methods are rudimentary, essentially a text-based search in the documentation, thus users cannot find the relevant models. This paper presents ProbeLog, a method for retrieving classification models that can recognize a target concept, such as "Dog", without access to model metadata or training data. Differently from previous probing methods, ProbeLog computes a descriptor for each output dimension (logit) of each model, by observing its responses on a fixed set of inputs (probes). Our method supports both logit-based retrieval ("find more logits like this") and zero-shot, text-based retrieval ("find all logits corresponding to dogs"). As probing-based representations require multiple costly feedforward passes through the model, we develop a method, based on collaborative filtering, that reduces the cost of encoding repositories by 3x. We demonstrate that ProbeLog achieves high retrieval accuracy, both in real-world and fine-grained search tasks and is scalable to full-size repositories.

Paper page on Hugging Face · arXiv

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