Feature Extraction
Transformers
Safetensors
seqscreen
proteins
molecules
bioinformatics
drug-discovery
custom_code
Instructions to use SaeedLab/BindScreen-Finetuning-LIT_PCBA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaeedLab/BindScreen-Finetuning-LIT_PCBA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SaeedLab/BindScreen-Finetuning-LIT_PCBA", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SaeedLab/BindScreen-Finetuning-LIT_PCBA", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from SaeedLab/BindScreen-Finetuning-LIT_PCBA: direct link, hf CLI and curl.
- Browser
- Download file 8.93 MB
-
https://huggingface.co/SaeedLab/BindScreen-Finetuning-LIT_PCBA/resolve/main/model.safetensors
- Command line
-
hf download hf://SaeedLab/BindScreen-Finetuning-LIT_PCBA/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/SaeedLab/BindScreen-Finetuning-LIT_PCBA/resolve/main/model.safetensors
8.93 MB
- Xet hash:
- e7d54950cdd4e25fe2215ddfde9108288310953b6e3cdef5bfd66bbf0285c965
- Size of remote file:
- 8.93 MB
- SHA256:
- 7c4a55beefcc43242daf84849856898dcf03039d21a5849c1878e7a2edc05042
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