Update README.md

README.md

@@ -149,7 +149,7 @@ Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
 Fine-tuned using the **Google Colab Notebook*** Version of **ai-toolkit**.<br>
 I've used A100 via Colab Pro.
 However, training SD3.5 may potentially work with Free Colab or lower Vram in general:<br> 
-Especially if one were to use lower rank (try 4 or 8), dataset size (in terms of caching/bucketing/pre-loading impacts), 1 batch size, Adamw8bit optimizer, 512 resolution, maybe adding the "lowvram, true" argument, and plausibly specifying alternate quantization variants! 
+Especially if one were to use: say, *lower rank (try 4 or 8), dataset size (in terms of caching/bucketing/pre-loading impacts), 1 batch size, Adamw8bit optimizer, 512 resolution, maybe adding the /lowvram, true/ argument, and plausibly specifying alternate quantization variants.* <br>
 Generally, VRAM expenditures tend to be lower than for Flux during training. So, try it! I certainly will.<br>
 **To use on Colab**, modify a Flux template Notebook from [here](https://github.com/ostris/ai-toolkit/tree/main/notebooks) with parameters from Ostris' example config for SD3.5 [here](https://github.com/ostris/ai-toolkit/blob/main/config/examples/train_lora_sd35_large_24gb.yaml)!
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