Update README.md

README.md

@@ -83,11 +83,11 @@ If you wish to use the original data, please contact the original author directl
     
 ## Training Details
 
-We use QLora to train the base model.
+- We use QLora to train the base model.
 Quantized Low Rank Adapters (QLoRA) is an efficient technique that uses 4-bit quantized pre-trained language models to fine-tune 65 billion parameter models on a 48 GB GPU while significantly reducing memory usage. 
 The method uses NormalFloat 4-bit (NF4), a new data type that is theoretically optimal for normally distributed weights; Double Quantization, which further quantizes quantization constants to reduce average memory usage; and Paged Optimizers, which manage memory spikes during mini-batch processing, to increase memory efficiency without sacrificing performance.
 
-Also, we performed instruction tuning using the data that we collected and the kyujinpy/KOR-OpenOrca-Platypus-v3 dataset on the hugging face. 
+- Also, we performed instruction tuning using the data that we collected and the kyujinpy/KOR-OpenOrca-Platypus-v3 dataset on the hugging face. 
 Instruction tuning is learning in a supervised learning format that uses instructions and input data together as input and output data as a pair.