diff --git a/examples/kfto-sft-llm/sft.ipynb b/examples/kfto-sft-llm/sft.ipynb
index a4f0067e4..396d0c0a1 100644
--- a/examples/kfto-sft-llm/sft.ipynb
+++ b/examples/kfto-sft-llm/sft.ipynb
@@ -42,9 +42,9 @@
     "# Model\n",
     "model_name_or_path: Meta-Llama/Meta-Llama-3.1-8B-Instruct\n",
     "model_revision: main\n",
-    "torch_dtype: bfloat16\n",
+    "dtype: bfloat16\n",
     "attn_implementation: flash_attention_2    # one of eager (default), sdpa or flash_attention_2\n",
-    "use_liger: false                          # use Liger kernels\n",
+    "use_liger_kernel: false                          # use Liger kernels\n",
     "\n",
     "# PEFT / LoRA\n",
     "use_peft: true\n",
@@ -69,8 +69,7 @@
     "  append_concat_token: false              # add additional separator token\n",
     "\n",
     "# SFT\n",
-    "max_seq_length: 1024                      # max sequence length for model and packing of the dataset\n",
-    "dataset_batch_size: 1000                  # samples to tokenize per batch\n",
+    "max_length: 1024                      # max sequence length for model and packing of the dataset\n",
     "packing: false\n",
     "\n",
     "# Training\n",
@@ -175,7 +174,7 @@
     "        revision=model_args.model_revision,\n",
     "        trust_remote_code=model_args.trust_remote_code,\n",
     "        attn_implementation=model_args.attn_implementation,\n",
-    "        torch_dtype=model_args.torch_dtype,\n",
+    "        dtype=model_args.dtype,\n",
     "        use_cache=False if training_args.gradient_checkpointing or\n",
     "                           training_args.fsdp_config.get(\"activation_checkpointing\",\n",
     "                                                         False) else True,\n",