Create README.md

README.md

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+---
+license: mit
+language:
+- en
+---
+
+Load the model:
+
+```
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+model_name = "rnalm/446M_H_MM_best"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+
+# Move model to GPU
+model = model.cuda()
+```
+
+Inference without using the track prediction head:
+```
+# disable the track head in order to avoid providing the metadata
+model.model.predict_tracks = False
+inputs = tokenizer("ACGTACGT", return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(input_ids=inputs["input_ids"].cuda())
+
+outputs.last_hidden_state.shape
+# torch.Size([1, 8, 1024])
+
+outputs.seq_logits.shape
+# torch.Size([1, 8, 11])
+```
+
+Predict tracks using given metadata:
+```
+metadata = # path to tensor metadata
+# Enable track prediction mode
+model.model.predict_tracks = True
+
+# Forward pass
+with torch.no_grad():
+    outputs = model(
+        input_ids=inputs["input_ids"].cuda(),
+        metadata=metadata.cuda()
+    )
+
+outputs.track_yhat
+```
+
+Get metadata-dependent embeddings:
+```
+metadata = # path to tensor metadata
+# Enable track prediction mode
+model.model.predict_tracks = True
+
+# Forward pass
+with torch.no_grad():
+    outputs = model(
+        input_ids=inputs["input_ids"].cuda(),
+        metadata=metadata.cuda()
+    )
+
+outputs.last_hidden_state_track.shape
+# torch.Size([1, 8, 1024])
+```