diff --git a/tools/python/model_validation/perplexity_metrics.py b/tools/python/model_validation/perplexity_metrics.py
new file mode 100644
index 000000000..bbb0dd7ff
--- /dev/null
+++ b/tools/python/model_validation/perplexity_metrics.py
@@ -0,0 +1,44 @@
+from datasets import load_dataset
+import numpy as np
+import onnxruntime_genai as og
+import torch
+
+def get_wikitext2():
+    testdata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
+    return testdata
+
+def perplexity_eval(model_dir):
+    model = og.Model(f'{model_dir}')
+    tokenizer = og.Tokenizer(model)
+
+    dataset = get_wikitext2()
+
+    total_log_probs = 0
+    total_token_count = 0
+
+    for batch in dataset:
+        text = batch["text"]
+
+        input_ids = tokenizer.encode_batch([text])
+
+        params = og.GeneratorParams(model)
+        generator = og.Generator(model, params)
+
+        logits = generator.compute_logits("logits")
+
+        targets = np.roll(input_ids, -1, axis=1)
+
+        # Use LogSoftMax here
+        log_probs = torch.nn.functional.log_softmax(torch.tensor(logits), dim=1).numpy()
+
+        batch_size, seq_length = targets.shape
+        target_log_probs = log_probs[np.arange(batch_size)[:, None], np.arange(seq_length), targets]
+
+        total_log_probs += np.sum(target_log_probs)
+        total_token_count += targets.size
+
+    avg_log_prob = total_log_probs / total_token_count
+    perplexity = np.exp(-avg_log_prob)
+
+    print(f"The perplexity of {model_dir} is {perplexity}")
+    return perplexity
\ No newline at end of file
diff --git a/tools/python/model_validation/validation_config.json b/tools/python/model_validation/validation_config.json
index c7259481d..9e06f7114 100644
--- a/tools/python/model_validation/validation_config.json
+++ b/tools/python/model_validation/validation_config.json
@@ -2,23 +2,29 @@
     "models": [
         {
             "name": "Qwen/Qwen2.5-7B-Instruct",
-            "chat_template": "<|im_start|>\n <|user|> \n {input} <|im_end>\n'<|im_start|>assistant\n"
+            "chat_template": "<|im_start|>\n <|user|> \n {input} <|im_end>\n'<|im_start|>assistant\n",
+            "metrics": []
         },
         {
             "name": "meta-llama/Llama-2-7b-chat-hf",
-            "chat_template": "<s>[INST]<<SYS>>\n{input}<</SYS>>[INST]"
+            "chat_template": "<s>[INST]<<SYS>>\n{input}<</SYS>>[INST]",
+            "metrics": []
+
         },
         {
             "name": "mistralai/Mistral-7B-Instruct-v0.3",
-            "chat_template": " \"[INST] \" + {input} + \"[/INST]\""
+            "chat_template": " \"[INST] \" + {input} + \"[/INST]\"",
+            "metrics": []
         },
         {
             "name": "microsoft/Phi-3.5-mini-instruct",
-            "chat_template": "<|user|>\n{input} <|end|>\n<|assistant|>"
+            "chat_template": "<|user|>\n{input} <|end|>\n<|assistant|>",
+            "metrics": []
         },
         {
             "name": "google/gemma-2-2b-it",
-            "chat_template": "'<start_of_turn>' + <|user|> '\n' + {input}  + '<end_of_turn>\n"
+            "chat_template": "'<start_of_turn>' + <|user|> '\n' + {input}  + '<end_of_turn>\n",
+            "metrics": []
         }
     ],
     "inputs": [
diff --git a/tools/python/model_validation/validation_tool.py b/tools/python/model_validation/validation_tool.py
index fad36aa36..a798b5c18 100644
--- a/tools/python/model_validation/validation_tool.py
+++ b/tools/python/model_validation/validation_tool.py
@@ -4,6 +4,7 @@
 import json
 import os
 import pandas as pd
+from perplexity_metrics import perplexity_eval
 
 def create_table(output):
     df = pd.DataFrame(output, columns=['Model Name', 'Validation Completed', 'Exceptions / Failures'])
@@ -113,6 +114,9 @@ def validate_model(args, model_dict, model_dir):
             print(f'Failure after validation model {e}')
             exception = True
             output.append([model_dict["name"], validation_complete, e]) 
+
+        # function call out here?
+        perplexity_eval(output_path)
         
         if not exception:
             output.append([model_dict["name"], validation_complete, e])