Feature Extraction
Transformers
PyTorch
Safetensors
fimhawkes
time-series
temporal-point-processes
hawkes-processes
scientific-ml
custom_code
Instructions to use FIM4Science/fim-pp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FIM4Science/fim-pp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FIM4Science/fim-pp", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FIM4Science/fim-pp", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from FIM4Science/fim-pp: direct link, hf CLI and curl.
- Browser
- Download file 64.6 MB
-
https://huggingface.co/FIM4Science/fim-pp/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://FIM4Science/fim-pp/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/FIM4Science/fim-pp/resolve/main/pytorch_model.bin
64.6 MB
- Xet hash:
- c5df9d63d47fe9e341fe8747b7ba6aefcb762e72cd998c83db6c8b2a1e81efd0
- Size of remote file:
- 64.6 MB
- SHA256:
- c01bd55e9770f9df86a038c27b4997314f68a1b9abc565f8b983e171bbaf7fb6
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