diff --git a/examples/language-modeling/gemma_tuning.ipynb b/examples/language-modeling/gemma_tuning.ipynb
index 537c248f..9fbcaf79 100644
--- a/examples/language-modeling/gemma_tuning.ipynb
+++ b/examples/language-modeling/gemma_tuning.ipynb
@@ -250,7 +250,8 @@
    "outputs": [],
    "source": [
     "from transformers import AutoModelForCausalLM\n",
-    "model = AutoModelForCausalLM.from_pretrained(model_id, use_cache=False)"
+    "import torch\n",
+    "model = AutoModelForCausalLM.from_pretrained(model_id, use_cache=False, torch_dtype=torch.bfloat16)"
    ]
   },
   {
diff --git a/examples/language-modeling/llama_tuning.md b/examples/language-modeling/llama_tuning.md
index 13bd484e..9d38130a 100644
--- a/examples/language-modeling/llama_tuning.md
+++ b/examples/language-modeling/llama_tuning.md
@@ -53,7 +53,7 @@ model_id = "meta-llama/Meta-Llama-3-8B"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 # Add custom token for padding Llama
 tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})
-model = AutoModelForCausalLM.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
 ```
 
 To tune the model with the [Abirate/english_quotes](https://huggingface.co/datasets/Abirate/english_quotes) dataset, you can load it and obtain the `quote` column: