diff --git a/distilbert/README.md b/distilbert/README.md
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+# DistilBERT
+This truss runs the [DistilBERT](https://huggingface.co/docs/transformers/en/model_doc/distilbert) model as an endpoint on Baseten.
+
+## Deploy
+```
+pip install --upgrade truss
+truss push --publish # grab an api key from https://app.baseten.co/settings/api_keys
+```
+
+The deployment will take a few minutes the first. Once it's ready in the you UI you can proceed to calling the API.
+
+## Test
+```
+truss predict --published -d '{"text": "some text to embed"}'
+```
\ No newline at end of file
diff --git a/distilbert/config.yaml b/distilbert/config.yaml
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+
+model_name: DistilBert
+python_version: py310
+requirements_file: ./requirements.txt
+resources:
+  accelerator: T4
diff --git a/distilbert/model/__init__.py b/distilbert/model/__init__.py
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diff --git a/distilbert/model/model.py b/distilbert/model/model.py
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+import torch
+from transformers import AutoTokenizer, AutoModel
+
+
+class Model:
+    def __init__(self, **kwargs):
+        self._model = None
+
+    def load(self):
+        # Load model here and assign to self._model.
+        self.device = (
+            "cuda" if torch.cuda.is_available() else "cpu"
+        )  # the device to load the model onto
+
+        self._tokenizer = AutoTokenizer.from_pretrained(
+            "distilbert/distilbert-base-uncased", device=self.device
+        )
+        self._model = AutoModel.from_pretrained(
+            "distilbert/distilbert-base-uncased",
+            torch_dtype=torch.float16,
+        ).to(self.device)
+
+    def predict(self, model_input):
+        # Run model inference here
+        
+        text = model_input.get("text")
+
+        encoded_input = self._tokenizer(text, return_tensors='pt').to(self.device)
+        
+        return self._model(**encoded_input).last_hidden_state.tolist()
diff --git a/distilbert/requirements.txt b/distilbert/requirements.txt
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+++ b/distilbert/requirements.txt
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+hf-transfer==0.1.6
+torch==2.2.2
+transformers==4.40.0