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Punctuation, grammar, and spelling fixes.

Author: AnnaMHuff

articles/ai-services/openai/how-to/fine-tuning-direct-preference-optimization.md

@@ -56,7 +56,7 @@ Training datasets must be in `jsonl` format:
 
 Users can use preference fine tuning with base models as well as models that have already been fine-tuned using supervised fine-tuning as long as they are of a supported model/version.
 
-## How to use direct preference optimization fine-tuning?
+## How to use direct preference optimization fine-tuning
 
    :::image type="content" border="true" source="/azure/ai-services/openai/media/fine-tuning/preference-optimization.gif" alt-text="GIF of preference optimization fine-tuning steps.":::
 

articles/ai-services/openai/how-to/fine-tuning-safety-evaluation.md

@@ -48,7 +48,7 @@ If a model is found to generate output containing content detected as harmful at
 This model is unable to be deployed. Model evaluation identified that this fine tuned model scores above acceptable thresholds for [Violence, Self Harm]. Please review your training data set and resubmit the job.
 ```
 
-   :::image type="content" source="../media/fine-tuning/failure.png" alt-text="Screenshot of a failed fine-tuning job due to safety evaluation" lightbox="../media/fine-tuning/failure.png":::
+   :::image type="content" source="../media/fine-tuning/failure.png" alt-text="Screenshot of a failed fine-tuning job due to safety evaluation." lightbox="../media/fine-tuning/failure.png":::
 
 As with data evaluation, the model is evaluated automatically within your fine-tuning job as part of providing the fine-tuning capability. Only the resulting assessment (deployable or not deployable) is logged by the service. If deployment of the fine-tuned model fails due to the detection of harmful content in model outputs, you won't be charged for the training run.
 

articles/ai-services/openai/includes/fine-tuning-openai-in-ai-studio.md

@@ -101,7 +101,7 @@ In addition to the JSONL format, training and validation data files must be enco
 
 ### Datasets size consideration
 
-The more training examples you have, the better. Fine tuning jobs will not proceed without at least 10 training examples, but such a small number isn't enough to noticeably influence model responses. It is best practice to provide hundreds, if not thousands, of training examples to be successful. It's recommened to start with 50 weel-crafted training data.
+The more training examples you have, the better. Fine tuning jobs will not proceed without at least 10 training examples, but such a small number isn't enough to noticeably influence model responses. It is best practice to provide hundreds, if not thousands, of training examples to be successful. It's recommended to start with 50 well-crafted training data.
 
 In general, doubling the dataset size can lead to a linear increase in model quality. But keep in mind, low quality examples can negatively impact performance. If you train the model on a large amount of internal data, without first pruning the dataset for only the highest quality examples you could end up with a model that performs much worse than expected.
 

articles/ai-services/openai/includes/fine-tuning-python.md

@@ -255,7 +255,7 @@ print(response.model_dump_json(indent=2))
 
 ### List fine-tuning events
 
-To examine the individual fine-tuning events that were generated during training. You might need to upgrade your OpenAI client library to the latest version with `pip install openai --upgrade` to run this command.
+To examine the individual fine-tuning events that were generated during training, you might need to upgrade your OpenAI client library to the latest version with `pip install openai --upgrade` to run this command.
 
 ```python
 response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id, limit=10)

articles/ai-services/openai/includes/fine-tuning-studio.md

@@ -43,7 +43,7 @@ Take a moment to review the fine-tuning workflow for using Azure AI Foundry port
 
 1. Prepare your training and validation data.
 1. Use the **Create custom model** wizard in Azure AI Foundry portal to train your custom model.
-    1. Select a base mode.
+    1. Select a base model.
     1. [Choose your training data](#choose-your-training-data).
     1. Optionally, [choose your validation data](#choose-your-validation-data).
     1. Optionally, [configure task parameters](#configure-task-parameters) for your fine-tuning job.