acrolinx and heading fixes

Author: v-dirichards

articles/ai-studio/how-to/deploy-models-phi-3.md

@@ -1476,9 +1476,9 @@ Phi-3 family models deployed to managed compute are billed based on core hours o
 
 It is a good practice to start with a low number of instances and scale up as needed. You can monitor the cost of the compute instance in the Azure portal.
 
-## Sample Notebook
+## Sample notebook
 
-You can use this [sample notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/finetuning/standalone/chat-completion/chat_completion_with_model_as_service.ipynb)  to create a standalone fine-tuning job to enhance a model's ability to summarize dialogues between two people using the Samsum dataset. The training data utilized is the ultrachat_200k dataset, which is divided into four splits suitable for supervised fine-tuning (sft) and generation ranking (gen). The notebook employs the available Azure AI models for the chat-completion task (If you would like to use a different model than whats used in the notebook, you can replace the model name). The notebook includes setting up prerequisites, selecting a model to fine-tune, creating training and validation datasets, configuring and submitting the fine-tuning job, and finally, creating a serverless deployment using the fine-tuned model for sample inference.
+You can use this [sample notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/finetuning/standalone/chat-completion/chat_completion_with_model_as_service.ipynb)  to create a standalone fine-tuning job to enhance a model's ability to summarize dialogues between two people using the Samsum dataset. The training data utilized is the ultrachat_200k dataset, which is divided into four splits suitable for supervised fine-tuning (sft) and generation ranking (gen). The notebook employs the available Azure AI models for the chat-completion task (If you would like to use a different model than what's used in the notebook, you can replace the model name). The notebook includes setting up prerequisites, selecting a model to fine-tune, creating training and validation datasets, configuring and submitting the fine-tuning job, and finally, creating a serverless deployment using the fine-tuned model for sample inference.
 
 ## Related content
 

articles/ai-studio/how-to/fine-tune-model-llama.md

@@ -269,9 +269,9 @@ Each time a project subscribes to a given offer from the Azure Marketplace, a ne
 
 For more information on how to track costs, see [monitor costs for models offered throughout the Azure Marketplace](./costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace).
 
-## Sample Notebook
+## Sample notebook
 
-You can use this [sample notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/finetuning/standalone/chat-completion/chat_completion_with_model_as_service.ipynb)  to create a standalone fine-tuning job to enhance a model's ability to summarize dialogues between two people using the Samsum dataset. The training data utilized is the ultrachat_200k dataset, which is divided into four splits suitable for supervised fine-tuning (sft) and generation ranking (gen). The notebook employs the available Azure AI models for the chat-completion task (If you would like to use a different model than whats used in the notebook, you can replace the model name). The notebook includes setting up prerequisites, selecting a model to fine-tune, creating training and validation datasets, configuring and submitting the fine-tuning job, and finally, creating a serverless deployment using the fine-tuned model for sample inference.
+You can use this [sample notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/finetuning/standalone/chat-completion/chat_completion_with_model_as_service.ipynb)  to create a standalone fine-tuning job to enhance a model's ability to summarize dialogues between two people using the Samsum dataset. The training data utilized is the ultrachat_200k dataset, which is divided into four splits suitable for supervised fine-tuning (sft) and generation ranking (gen). The notebook employs the available Azure AI models for the chat-completion task (If you would like to use a different model than what's used in the notebook, you can replace the model name). The notebook includes setting up prerequisites, selecting a model to fine-tune, creating training and validation datasets, configuring and submitting the fine-tuning job, and finally, creating a serverless deployment using the fine-tuned model for sample inference.
 
 ## Content filtering
 

articles/ai-studio/how-to/fine-tune-phi-3.md

@@ -232,9 +232,9 @@ You can delete a fine-tuned model from the fine-tuning model list in [Azure AI S
 
 Phi models fine-tuned as a service are offered by Microsoft and integrated with Azure AI Studio for use. You can find the pricing when [deploying](./deploy-models-phi-3.md) or fine-tuning the models under the Pricing and terms tab on deployment wizard.
 
-## Sample Notebook
+## Sample notebook
 
-You can use this [sample notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/finetuning/standalone/chat-completion/chat_completion_with_model_as_service.ipynb)  to create a standalone fine-tuning job to enhance a model's ability to summarize dialogues between two people using the Samsum dataset. The training data utilized is the ultrachat_200k dataset, which is divided into four splits suitable for supervised fine-tuning (sft) and generation ranking (gen). The notebook employs the available Azure AI models for the chat-completion task (If you would like to use a different model than whats used in the notebook, you can replace the model name). The notebook includes setting up prerequisites, selecting a model to fine-tune, creating training and validation datasets, configuring and submitting the fine-tuning job, and finally, creating a serverless deployment using the fine-tuned model for sample inference.
+You can use this [sample notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/finetuning/standalone/chat-completion/chat_completion_with_model_as_service.ipynb)  to create a standalone fine-tuning job to enhance a model's ability to summarize dialogues between two people using the Samsum dataset. The training data utilized is the ultrachat_200k dataset, which is divided into four splits suitable for supervised fine-tuning (sft) and generation ranking (gen). The notebook employs the available Azure AI models for the chat-completion task (If you would like to use a different model than what's used in the notebook, you can replace the model name). The notebook includes setting up prerequisites, selecting a model to fine-tune, creating training and validation datasets, configuring and submitting the fine-tuning job, and finally, creating a serverless deployment using the fine-tuned model for sample inference.
 
 ## Content filtering