The success of powerful open source Large Language Models (LLMs) has enabled
the community to create a vast collection of post-trained models adapted to
specific tasks and domains. However, navigating and understanding these models
remains challenging due to inconsistent metadata and unstructured repositories.
We introduce Delta Activations, a method to represent finetuned models as
vector embeddings by measuring shifts in their internal activations relative to
a base model. This representation allows for effective clustering by domain and
task, revealing structure in the model landscape. Delta Activations also
demonstrate desirable properties: it is robust across finetuning settings and
exhibits an additive property when finetuning datasets are mixed. In addition,
we show that Delta Activations can embed tasks via few-shot finetuning, and
further explore its use for model selection and merging. We hope Delta
Activations can facilitate the practice of reusing publicly available models.
Code is available at https://github.com/OscarXZQ/delta_activations.