diff --git a/examples/llama3-8b-ec2/llama3_ec2.py b/examples/llama3-8b-ec2/llama3_ec2.py
index 524ca41be..542ad0888 100644
--- a/examples/llama3-8b-ec2/llama3_ec2.py
+++ b/examples/llama3-8b-ec2/llama3_ec2.py
@@ -1,15 +1,17 @@
-# # Deploy Llama2 13B Chat Model Inference on AWS EC2
+# # Deploy Llama3 8B Chat Model Inference on AWS EC2
 
 # This example demonstrates how to deploy a
-# [LLama2 13B model from Hugging Face](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)
+# [LLama3 8B model from Hugging Face](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
 # on AWS EC2 using Runhouse.
 #
+# Make sure to sign the waiver on the model page so that you can access it.
+#
 # ## Setup credentials and dependencies
 #
 # Optionally, set up a virtual environment:
 # ```shell
-# $ conda create -n llama-demo-apps python=3.8
-# $ conda activate llama-demo-apps
+# $ conda create -n llama3-rh
+# $ conda activate llama3-rh
 # ```
 # Install the few required dependencies:
 # ```shell
@@ -22,7 +24,7 @@
 # $ aws configure
 # $ sky check
 # ```
-# We'll be downloading the Llama2 model from Hugging Face, so we need to set up our Hugging Face token:
+# We'll be downloading the Llama3 model from Hugging Face, so we need to set up our Hugging Face token:
 # ```shell
 # $ export HF_TOKEN=<your huggingface token>
 # ```
@@ -55,7 +57,7 @@ def load_model(self):
         self.pipeline = transformers.pipeline(
             "text-generation",
             model=self.model_id,
-            model_kwargs={"torch_dtype": torch.bfloat16},
+            model_kwargs=self.model_kwargs,
             device="cuda",
         )
 
@@ -108,7 +110,6 @@ def predict(self, prompt_text, **inf_kwargs):
     gpu = rh.cluster(
         name="rh-a10x", instance_type="A10G:1", memory="32+", provider="aws"
     )
-    # gpu.restart_server(restart_ray=True)
 
     # Next, we define the environment for our module. This includes the required dependencies that need
     # to be installed on the remote machine, as well as any secrets that need to be synced up from local to remote.
@@ -124,38 +125,34 @@ def predict(self, prompt_text, **inf_kwargs):
             "safetensors",
             "scipy",
         ],
-        secrets=["huggingface"],  # Needed to download Llama2
-        name="llama2inference",
+        secrets=["huggingface"],  # Needed to download Llama3 from HuggingFace
+        name="llama3inference",
         working_dir="./",
     )
 
     # Finally, we define our module and run it on the remote cluster. We construct it normally and then call
     # `get_or_to` to run it on the remote cluster. Using `get_or_to` allows us to load the exiting Module
-    # by the name `llama-13b-model` if it was already put on the cluster. If we want to update the module each
+    # by the name `llama3-8b-model` if it was already put on the cluster. If we want to update the module each
     # time we run this script, we can use `to` instead of `get_or_to`.
     #
     # Note that we also pass the `env` object to the `get_or_to` method, which will ensure that the environment is
     # set up on the remote machine before the module is run.
     remote_hf_chat_model = HFChatModel(
-        load_in_4bit=True,  # Ignored right now
-        torch_dtype=torch.bfloat16,  # Ignored right now
-        device_map="auto",  # Ignored right now
-    ).get_or_to(gpu, env=env, name="llama-13b-model")
+        torch_dtype=torch.bfloat16,
+    ).get_or_to(gpu, env=env, name="llama3-8b-model")
 
     # ## Calling our remote function
     #
     # We can call the `predict` method on the model class instance if it were running locally.
     # This will run the function on the remote cluster and return the response to our local machine automatically.
     # Further calls will also run on the remote machine, and maintain state that was updated between calls, like
-    # `self.model` and `self.tokenizer`.
-    prompt = "Who are you?"
-    print(remote_hf_chat_model.predict(prompt))
-    # while True:
-    #     prompt = input(
-    #         "\n\n... Enter a prompt to chat with the model, and 'exit' to exit ...\n"
-    #     )
-    #     if prompt.lower() == "exit":
-    #         break
-    #     output = remote_hf_chat_model.predict(prompt)
-    #     print("\n\n... Model Output ...\n")
-    #     print(output)
+    # `self.pipeline`.
+    while True:
+        prompt = input(
+            "\n\n... Enter a prompt to chat with the model, and 'exit' to exit ...\n"
+        )
+        if prompt.lower().strip() == "exit":
+            break
+        output = remote_hf_chat_model.predict(prompt)
+        print("\n\n... Model Output ...\n")
+        print(output)