| --- |
| tags: |
| - time-series |
| - temporal-point-processes |
| - hawkes-processes |
| - scientific-ml |
| license: mit |
| library_name: transformers |
| --- |
| |
| # 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: |
|
|
| ```python |
| 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: |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
