diff --git a/06_gpu_and_ml/vllm_inference.py b/06_gpu_and_ml/vllm_inference.py
index 09e7d89ff..a6b9ff4be 100644
--- a/06_gpu_and_ml/vllm_inference.py
+++ b/06_gpu_and_ml/vllm_inference.py
@@ -1,4 +1,4 @@
-# # Fast inference with vLLM (Llama 2 13B)
+# # Fast inference with vLLM (Mistral 7B)
 #
 # In this example, we show how to run basic inference, using [`vLLM`](https://github.com/vllm-project/vllm)
 # to take advantage of PagedAttention, which speeds up sequential inferences with optimized key-value caching.
@@ -6,9 +6,9 @@
 # `vLLM` also supports a use case as a FastAPI server which we will explore in a future guide. This example
 # walks through setting up an environment that works with `vLLM ` for basic inference.
 #
-# We are running the Llama 2 13B model here, and you can expect 30 second cold starts and well over 100 tokens/second.
-# The larger the batch of prompts, the higher the throughput. For example, with the 60 prompts below,
-# we can produce 24k tokens in 39 seconds, which is around 600 tokens/second.
+# We are running the [Mistral 7B Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) model here, which is an instruct fine-tuned version of Mistral's 7B model best fit for conversation. 
+# You can expect 20 second cold starts and well over 100 tokens/second. The larger the batch of prompts, the higher the throughput. 
+# For example, with the 60 prompts below, we can produce 19k tokens in 15 seconds, which is around 1.25k tokens/second.
 #
 # To run
 # [any of the other supported models](https://vllm.readthedocs.io/en/latest/models/supported_models.html),
@@ -30,12 +30,7 @@
 # advantage of Modal's internal filesystem for faster cold starts.
 #
 # ### Download the weights
-#
-# Since the weights are gated on HuggingFace, we must request access in two places:
-# - on the [model card page](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)
-# - accept the license [on the Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/).
-#
-# Next, [create a HuggingFace access token](https://huggingface.co/settings/tokens).
+# Make sure you have created a [HuggingFace access token](https://huggingface.co/settings/tokens).
 # To access the token in a Modal function, we can create a secret on the [secrets page](https://modal.com/secrets).
 # Now the token will be available via the environment variable named `HUGGINGFACE_TOKEN`. Functions that inject this secret will have access to the environment variable.
 #
@@ -46,13 +41,14 @@ def download_model_to_folder():
     from huggingface_hub import snapshot_download
 
     snapshot_download(
-        "meta-llama/Llama-2-13b-chat-hf",
+        BASE_MODEL,
         local_dir="/model",
         token=os.environ["HUGGINGFACE_TOKEN"],
     )
 
 
 MODEL_DIR = "/model"
+BASE_MODEL = "mistralai/Mistral-7B-Instruct-v0.1"
 
 # ### Image definition
 # We’ll start from a Dockerhub image recommended by `vLLM`, upgrade the older
@@ -65,10 +61,11 @@ def download_model_to_folder():
     .pip_install(
         "torch==2.0.1", index_url="https://download.pytorch.org/whl/cu118"
     )
-    # Pin vLLM to 07/19/2023
-    .pip_install(
-        "vllm @ git+https://github.com/vllm-project/vllm.git@bda41c70ddb124134935a90a0d51304d2ac035e8"
-    )
+    .apt_install("git")
+    # Download latest version of vLLM
+    .run_commands(
+        "git clone https://github.com/vllm-project/vllm.git",
+        "cd vllm && pip install -e .",)
     # Use the barebones hf-transfer package for maximum download speeds. No progress bar, but expect 700MB/s.
     .pip_install("hf-transfer~=0.1")
     .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})