added download instructions

README.md

@@ -14,3 +14,57 @@ tags:
 
 ViT-large (300M parameters) trained on a diverse neuropathology dataset.
 
+
+## Model Usage
+
+To get started, first clone the repository with this command:
+```bash
+  git clone --no-checkout https://huggingface.co/MountSinaiCompPath/neuroFM_HE20x && cd neuroFM_HE20x && git sparse-checkout init --no-cone && git sparse-checkout set '/*' '!*.bin' && git checkout
+```
+
+Now you can use the following code:
+```python
+from PIL import Image
+import numpy as np
+import vision_transformer
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from huggingface_hub import PyTorchModelHubMixin
+
+class neuroFM_HE20x(nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super().__init__()
+	vit_kwargs = dict(
+            img_size=224,
+            patch_size=14,
+            init_values=1.0e-05,
+            ffn_layer='swiglufused',
+            block_chunks=4,
+            qkv_bias=True,
+            proj_bias=True,
+            ffn_bias=True,
+        )
+        self.encoder = vision_transformer.__dict__['vit_large'](**vit_kwargs)
+    
+    def forward(self, x):
+        return self.encoder(x)
+
+# Download model
+model = neuroFM_HE20x.from_pretrained("MountSinaiCompPath/neuroFM_HE20x")
+
+# Set up transform
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+])
+
+# Image
+img = np.random.randint(0, 256, size=224*224*3).reshape(224,224,3).astype(np.uint8)
+img = Image.fromarray(img)
+img = transform(img).unsqueeze(0)
+
+# Inference
+with torch.no_grad():
+    h = model(img)
+```