FIM-PP Model Card

FIM-PP is the Foundation Inference Model for marked temporal point processes. It infers conditional intensity functions from a context set of event sequences and supports zero-shot use as well as downstream fine-tuning.

Loading

Install the fim package first, then load the model with Transformers:

from transformers import AutoModel

model = AutoModel.from_pretrained("FIM4Science/FIM-PP", trust_remote_code=True)
model.eval()

Notes

• The released checkpoint is configured for up to 22 event marks.
• The model expects Hawkes-style context and inference tensors as described in the OpenFIM point-process tutorial.
• If needed, the lower-level fallback remains available through fim.models.hawkes.FIMHawkes.load_model(...).

Reference

If you use this model, please cite:

@inproceedings{fim_pp,
  title={In-Context Learning of Temporal Point Processes with Foundation Inference Models},
  author={David Berghaus and Patrick Seifner and Kostadin Cvejoski and Cesar Ojeda and Ramses J. Sanchez},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://openreview.net/forum?id=h9HwUAODFP}
}