Create README.md

README.md

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+# Model Card for Gluformer Blood Glucose Prediction Model
+
+This model uses past continuous glucose monitor (CGM) values to predict values for the next hour.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Renat Sergazinov, Mohammadreza Armandpour, Irina Gaynanova
+- **Funded by:** Texas A&M University
+- **Shared by:** Nat Jeffries
+- **Model type:** Time series encoder-decoder Transformer
+
+### Model Sources [optional]
+
+- **Repository:** [Github](https://github.com/mrsergazinov/gluformer)
+- **Paper:** [Arxiv](https://arxiv.org/pdf/2209.04526)
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```
+from transformers import AutoModel, AutoConfig
+from datetime import timedelta, datetime
+
+model = AutoModel.from_pretrained('njeffrie/Gluformer', trust_remote_code=True)
+config = AutoConfig.from_pretrained('njeffrie/Gluformer', trust_remote_code=True)
+
+# Dummy input and timestamp values.
+input_glucose = [100.0 for _ in range(config.len_seq)]
+timestamps = [datetime(2025, 7, 25, 0, 0) + timedelta(minutes=5 * i) for i in range(len(input_glucose))]
+subject_id = 0
+
+pred, log_var = model(subject_id, timestamps, input_glucose)
+```
+
+Predictions will be predicted future glucose values in 5 minute increments. Log var indicates confidence. See the paper for more details.