Update README.md

README.md

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 # Accuracy Recovery Adapters
 
 This repo contains various accuracy recovery adapters (ARAs) that I have trained, primarialy for use with [AI Toolkit](https://github.com/ostris/ai-toolkit).
-An ARA is a LoRA that is trained via student teacher training with the student being quantized dow a low precision and the teacher having a high precision.
+An ARA is a LoRA that is trained via student teacher training with the student being quantized down to a low precision and the teacher having a high precision.
 The goal is to have a side chain LoRA, at bfloat16, that runs parallel to highly quantized layers in a network to compensate for the loss in precision that happens
 when weights are quantized. The training is done on a per layer basis in order to match the parent output as much as possible. 
 
@@ -10,7 +10,7 @@ is now possible to train a LoRA on top of it, with 1 MP images, on a 24 GB GPU,
 
 I have found the sweet spot, at least for [Qwen-Image](https://huggingface.co/Qwen/Qwen-Image), is 3 bit quantization with a rank 16 adapter.
 
-More info, links, training scripts, AI Toolkit example configs, and adapters to some soon.
+More info, examples, links, training scripts, AI Toolkit example configs, and adapters to some soon.
 
 ## Qwen-Image 3 bit quantization