diff --git a/examples/perplexity.py b/examples/perplexity.py
new file mode 100644
index 000000000..08594b667
--- /dev/null
+++ b/examples/perplexity.py
@@ -0,0 +1,51 @@
+import numpy as np
+from datasets import load_dataset
+from transformers import AutoTokenizer
+from aphrodite import LLM, SamplingParams
+
+# Load the wikitext2 dataset.
+dataset = load_dataset('wikitext', 'wikitext-2-raw-v1')
+
+# Get the first 2000 elements from the 'train' split.
+prompts = dataset['train']['text'][:2000]
+
+model_id = "mistralai/Mistral-7B-Instruct-v0.2"
+# Create a tokenizer.
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Tokenize the prompts and discard or truncate any prompts longer than 2048 tokens.
+tokenized_prompts = [tokenizer.encode(prompt, truncation=True,
+                                      max_length=4096) for prompt in prompts]
+
+# Detokenize the prompts.
+detokenized_prompts = [tokenizer.decode(tokens
+                                        ) for tokens in tokenized_prompts]
+
+# Create a sampling params object.
+sampling_params = SamplingParams(
+    temperature=0.0,
+    ignore_eos=True,
+    max_tokens=10,
+    skip_special_tokens=False,
+    spaces_between_special_tokens=False,
+    logprobs=1,
+    prompt_logprobs=1,
+)
+
+# Create an LLM.
+llm = LLM(model=model_id)
+
+# Generate texts from the detokenized prompts.
+outputs = llm.generate(detokenized_prompts, sampling_params)
+
+# Calculate the perplexity.
+all_logprobs = []
+for output in outputs:
+    all_logprobs.extend([next(iter(lp.values())) for lp in output.prompt_logprobs[1:]])
+
+all_logprobs = np.array([lp.logprob for lp in all_logprobs])
+# NOTE: we need to divide by 2 to match the perplexity results
+# for the same model on llama.cpp. I'm unsure if this
+# approach to ppx measurement is correct.
+perplexity = (np.exp(-all_logprobs.mean())) / 2
+print(f"Perplexity: {perplexity}")