Model reference · open weights
fim-pp is an open-weight embedding model from FIM4Science. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | FIM4Science |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 16M |
| Runs with | transformers |
| Released | 2026-03-27 |
| Popularity | 558 downloads / month |
| Licence | Open weights |
About
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.
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()
fim.models.hawkes.FIMHawkes.load_model(...).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}
}
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys fim-pp for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (fim-pp below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"fim-pp","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.