Feature Extraction
Transformers
Safetensors
lumen
pathology
vision-language
histopathology
lora
zero-shot
custom_code
Instructions to use digitalpathologybern/Lumen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digitalpathologybern/Lumen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="digitalpathologybern/Lumen", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("digitalpathologybern/Lumen", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Drop the SHA-256 line from the model card
Verified signatureThe digest is still carried by SHA256SUMS and adapter_provenance.json, which is where a checksum belongs. Weights unchanged.78d64de
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Refuse unexpected forward kwargs, and document the loading warning
Verified signatureforward() took **kwargs and dropped anything it did not recognise, so the conventional pixel_values=/input_ids= returned a dict of Nones with no error. It now raises naming the arguments it does take. The card gained a runnable inference example and an explanation of the 'newly initialized' warning, which is expected: the backbones are withheld by design and all 455 vision and 199 text tensors were verified identical to the public releases. Weights unchanged, sha256 3559c771...12ec91e -
Lumen alignment: rank-4 LoRA adapters, projection heads, logit scale
Verified signature2,984,961 parameters exported from the checkpoint the manuscript reports (registry key odyssey_retrain_quilt). The 740,721,664 frozen Virchow2 and BioMedBERT parameters are withheld and come from their own repositories. model.safetensors sha256 3559c771a769286939a0916040e62a48cc25afe7c8bcd887ab3bebd5914c3c892076468