diff --git a/litgpt/finetune/lora.py b/litgpt/finetune/lora.py
index bb60b2d180..e1804c7b18 100644
--- a/litgpt/finetune/lora.py
+++ b/litgpt/finetune/lora.py
@@ -3,6 +3,7 @@
 import math
 import os
 import time
+import random
 from pathlib import Path
 from pprint import pprint
 from typing import Dict, List, Literal, Optional, Tuple, Union
@@ -235,10 +236,13 @@ def fit(
     data: DataModule,
 ) -> None:
     tokenizer = Tokenizer(checkpoint_dir)
-    longest_seq_length, longest_seq_ix = get_longest_seq_length(train_dataloader.dataset)
+
+    longest_seq_length_train, longest_seq_ix = get_longest_seq_length(train_dataloader.dataset)
+    longest_seq_length_val, longest_seq_ix = get_longest_seq_length(val_dataloader.dataset)
+    longest_seq_length = longest_seq_length_train if longest_seq_length_train > longest_seq_length_val else longest_seq_length_val
     model.max_seq_length = min(longest_seq_length, train.max_seq_length or float("inf"))
     fabric.print(
-        f"The longest sequence length in the train data is {longest_seq_length}, the model's maximum sequence length is"
+"The longest sequence lengths are {longest_seq_length_train} in training data and {longest_seq_length_val} in validation data. The model's maximum sequence length is"
         f" {model.max_seq_length} and context length is {model.config.block_size}"
     )
 
@@ -344,7 +348,12 @@ def validate(
     val_loss = losses.mean()
 
     # produce an example:
-    instruction = "Recommend a movie for me to watch during the weekend and explain the reason."
+    rand = random.randint(0, 50)
+    try:
+        instruction = val_dataloader.dataset.data[rand]["instruction"]
+    except Exception as e:
+        print(f"Import of validation data failed: {e}")
+        instruction = "Recommend a movie for me to watch during the weekend and explain the reason."
     fabric.print(instruction)
     prompt = data.prompt_style.apply(instruction)
     encoded = tokenizer.encode(prompt, device=fabric.device)