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@@ -11,7 +11,7 @@ Paper: https://arxiv.org/abs/2106.09685 <br>
 
 *Update 2/2023: LoRA is now supported by the [State-of-the-art Parameter-Efficient Fine-Tuning (PEFT)](https://github.com/huggingface/peft) library by Hugging Face.*
 
-LoRA reduces the number of trainable parameters by learning pairs of rank-decompostion matrices while freezing the original weights.
+LoRA reduces the number of trainable parameters by learning pairs of rank-decomposition matrices while freezing the original weights.
 This vastly reduces the storage requirement for large language models adapted to specific tasks and enables efficient task-switching during deployment all without introducing inference latency.
 LoRA also outperforms several other adaptation methods including adapter, prefix-tuning, and fine-tuning.