update

Author: mrbullwinkle

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

@@ -49,7 +49,7 @@ We use LoRA, or low rank approximation, to fine-tune models in a way that reduce
 
 ## Vision fine-tuning
 
-Fine-tuning is also possible with images in your JSONL files. Just as you can send one or many image inputs to chat completions, you can include those same message types within your training data. Images can be provided either as publicly available  URLs or data URIs containing [base64 encoded images](/azure/ai-services/openai/how-to/gpt-with-vision?tabs=rest#call-the-chat-completion-apis). 
+Fine-tuning is also possible with images in your JSONL files. Just as you can send one or many image inputs to chat completions, you can include those same message types within your training data. Images can be provided either as publicly accessible URLs or data URIs containing [base64 encoded images](/azure/ai-services/openai/how-to/gpt-with-vision?tabs=rest#call-the-chat-completion-apis). 
 
 ### Image dataset requirements
 
@@ -129,7 +129,7 @@ Your training files might get quite large. You can upload files up to 8 GB in mu
 
 **Reducing training cost**
 
-If you set the detail parameter for an image to low, the image is resized to 512 by 512 pixels and is only represented by 85 tokens regardless of its size. This will reduce the cost of training. See here for more information.
+If you set the detail parameter for an image to low, the image is resized to 512 by 512 pixels and is only represented by 85 tokens regardless of its size. This will reduce the cost of training.
 
 ```json
 {