Update fine-tuning.md

Abbreviation "LoRA" mentioned as "low rank approximation" instead of the correct "low rank adaptation".

Author: aaronkossler

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

@@ -23,7 +23,7 @@ Azure OpenAI Service lets you tailor our models to your personal datasets by usi
 
 In contrast to few-shot learning, fine tuning improves the model by training on many more examples than can fit in a prompt, letting you achieve better results on a wide number of tasks. Because fine tuning adjusts the base model’s weights to improve performance on the specific task, you won’t have to include as many examples or instructions in your prompt. This means less text sent and fewer tokens processed on every API call, potentially saving cost, and improving request latency.
 
-We use LoRA, or low rank approximation, to fine-tune models in a way that reduces their complexity without significantly affecting their performance. This method works by approximating the original high-rank matrix with a lower rank one, thus only fine-tuning a smaller subset of *important* parameters during the supervised training phase, making the model more manageable and efficient. For users, this makes training faster and more affordable than other techniques.
+We use LoRA, or low rank adaptation, to fine-tune models in a way that reduces their complexity without significantly affecting their performance. This method works by approximating the original high-rank matrix with a lower rank one, thus only fine-tuning a smaller subset of *important* parameters during the supervised training phase, making the model more manageable and efficient. For users, this makes training faster and more affordable than other techniques.
 
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