Update README.md (#7)

- Update README.md (4a48e83d89feea890c2bf429a588b5377e34b6e8)


Co-authored-by: Saarthak Kapse <Saarthak-GenBio-AI@users.noreply.huggingface.co>

README.md

@@ -24,6 +24,41 @@ For more details:
 * [GenBio AI Blog Post](https://genbio.ai/genbio-pathfm)
 * [Paper](https://www.biorxiv.org/content/10.64898/2026.03.17.712534v1)
 
+## Usage
+ 
+GenBio-PathFM can be loaded directly via HuggingFace `AutoModel` (tested on `transformers==4.57.1`):
+ 
+```python
+from transformers import AutoModel
+from torchvision import transforms
+ 
+# Load model
+model = AutoModel.from_pretrained("genbio-ai/genbio-pathfm", trust_remote_code=True)
+model.eval()
+ 
+# Transform
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=(0.697, 0.575, 0.728),
+        std=(0.188, 0.240, 0.187),
+    ),
+])
+ 
+# Inference
+import torch
+from PIL import Image
+ 
+image = Image.open("path/to/image.png").convert("RGB")
+x = transform(image).unsqueeze(0)  # [1, 3, 224, 224]
+ 
+with torch.no_grad():
+    cls_features = model(x)  # [1, 4608]
+    # Or with patch tokens:
+    cls_features, patch_features = model.forward_with_patches(x)  # [1, 4608], [1, 196, 4608]
+```
+
 ## Abstract
 
 Recent advancements in histopathology foundation models (FMs) have largely been driven by scaling the training data, often utilizing massive proprietary datasets. However, the long-tailed distribution of morphological features in whole-slide images (WSIs) makes simple scaling inefficient, as common morphologies dominate the learning signal. We introduce GenBio-PathFM, a 1.1B-parameter FM that achieves state-of-the-art performance on public benchmarks while using a fraction of the training data required by current leading models. The efficiency of GenBio-PathFM is underpinned by two primary innovations: an automated data curation pipeline that prioritizes morphological diversity and a novel dual-stage learning strategy which we term JEDI (**JE**PA + **DI**NO). Across the THUNDER, HEST, and PathoROB benchmarks, GenBio-PathFM demonstrates state-of-the-art accuracy and robustness. GenBio-PathFM is the strongest open-weight model to date and the only state-of-the-art model trained exclusively on public data.