diff --git a/articles/gpt-oss/fine-tune-transfomers.ipynb b/articles/gpt-oss/fine-tune-transfomers.ipynb
index 7131dfe9dd..2f4edd9b18 100644
--- a/articles/gpt-oss/fine-tune-transfomers.ipynb
+++ b/articles/gpt-oss/fine-tune-transfomers.ipynb
@@ -190,10 +190,10 @@
       "source": [
         "|||\n",
         "| :---- | :--|\n",
-        "| `developer` | The developer message is used to provide custom instructions for the model (what we usually call the `system` role) |\n",
-        "| `user` | The user message is used to provide the input to the model |\n",
+        "| `developer` | The developer message is used to provide custom instructions for the model (what we usually call the `system` role). |\n",
+        "| `user` | The user message is used to provide the input to the model. |\n",
         "| `assistant` | Output by the model which can either be a tool call or a message output. The output might also be associated with a particular “channel” identifying what the intent of the message is. |\n",
-        "| `analysis` | These are messages that are being used by the model for its chain-of thought |\n",
+        "| `analysis` | These are messages that are being used by the model for its chain-of-thought |\n",
         "| `final` | Messages tagged in the final channel are messages intended to be shown to the end-user and represent the responses from the model. |\n",
         "| `messages` | The list of messages that combine the content of the above to produce a full conversation. This is the input to the model. |"
       ]
@@ -345,7 +345,7 @@
         "\n",
         "To do so, we will use a technique called [LoRA](https://huggingface.co/learn/llm-course/chapter11/4) (Low-Rank Adaptation) to fine-tune the model. This technique allows us to tune a few specific layers of the model, which is particularly useful for large models like `openai/gpt-oss-20b`.\n",
         "\n",
-        "First we need wrap the model as a `PeftModel` and define the LoRA configuration. We will use the `LoraConfig` class from the [PEFT library](https://github.com/huggingface/peft) to do this:"
+        "First we need to wrap the model as a `PeftModel` and define the LoRA configuration. We will use the `LoraConfig` class from the [PEFT library](https://github.com/huggingface/peft) to do this:"
       ]
     },
     {
@@ -604,7 +604,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "\n",
         "REASONING_LANGUAGE = \"Chinese\"  # or Hindi, or any other language...\n",
         "SYSTEM_PROMPT = f\"reasoning language: {REASONING_LANGUAGE}\"\n",
         "USER_PROMPT = \"What is the national symbol of Canada?\"\n",