---
tags:
- time-series
- temporal-point-processes
- hawkes-processes
- scientific-ml
license: mit
---

# FIM-PP paper checkpoints

This repository holds the checkpoints behind the real-data results of
["In-Context Learning of Temporal Point Processes with Foundation Inference Models"](https://openreview.net/forum?id=h9HwUAODFP) (ICLR 2026),
other than the pretrained model itself, which is [`FIM4Science/fim-pp`](https://huggingface.co/FIM4Science/fim-pp).

| Subfolder | Paper result | Description |
|---|---|---|
| `table1-finetuned/{taxi,taobao,stackoverflow,amazon,retweet}` | Table 1 and appendix N = 5/10, FIM-PP (f) | FIM-PP fine-tuned on the CDiff train split of each dataset |
| `table2-zero-shot` | Table 2, FIM-PP (zs) | Epoch 38 of the pretraining run. `FIM4Science/fim-pp` is epoch 36 and was used for Table 1 |
| `table2-finetuned/{taxi,taobao}` | Table 2, FIM-PP (f) | `table2-zero-shot` fine-tuned on the EasyTPP train split of each dataset |

Each subfolder contains `config.json`, `model-checkpoint.pth` and `model.safetensors`.

## Usage

Install [OpenFIM](https://github.com/FIM4Science/OpenFIM) and pass `<repo>/<subfolder>` as the checkpoint:

```python
from fim.models.hawkes import FIMHawkes, FIMHawkesConfig

FIMHawkesConfig.register_for_auto_class()
model = FIMHawkes.load_model("FIM4Science/fim-pp-paper-checkpoints/table1-finetuned/taxi")
```

The evaluation scripts, configs and a description of the evaluation protocol are in
[`scripts/hawkes/REPRODUCE_FIM_PP.md`](https://github.com/FIM4Science/OpenFIM/blob/main/scripts/hawkes/REPRODUCE_FIM_PP.md).

## Citation

```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}
}
```