Update README.md

README.md

@@ -65,39 +65,39 @@ Exciting times ahead as we delve into the MermaidLLama revolution! 🚀
 
 LoRA Rank
 Also called dimension count. Higher values = larger file, more content control. Smaller values = smaller file, less control. Use 4 or 8 for style, 128 or 256 to teach, 1024+ for fine-detail on big data. More VRAM is needed for higher ranks.
-2048
+- 2048
 
 LoRA Alpha
 This divided by the rank becomes the scaling of the LoRA. Higher means stronger. A good standard value is twice your Rank.
-4096
+- 4096
 
 Batch Size
 Global batch size. The two batch sizes together determine gradient accumulation (gradientAccum = batch / microBatch). Higher gradient accum values lead to better quality training.
-1
+- 1
 
 Micro Batch Size
 Per-device batch size (NOTE: multiple devices not yet implemented). Increasing this will increase VRAM usage.
-1
+- 1
 
 Cutoff Length
 Cutoff length for text input. Essentially, how long of a line of text to feed in at a time. Higher values require drastically more VRAM.
-4096
+- 4096
 
 Save every n steps
 If above 0, a checkpoint of the LoRA will be saved every time this many steps pass.
-1000
+- 1000
 
 Epochs
 Number of times every entry in the dataset should be fed into training. So 1 means feed each item in once, 5 means feed it in five times, etc.
-3
+- 3
 
 Learning Rate
 In scientific notation.
-1e-6
+- 1e-6
 
 LR Scheduler
 Learning rate scheduler - defines how the learning rate changes over time. "Constant" means never change, "linear" means to go in a straight line from the learning rate down to 0, cosine follows a curve, etc.
-cosine
+- cosine
 
 
 Target Modules