Update README.md

README.md

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 This model is a model that performed continued pre-training and fine-tuning (instruction tuning) using the depth up-scaling (DUS) technique disclosed by Upstage.
 
 ### DUS(Depth Up-Scaling) and continued pre-training
-Similar to the methodology disclosed in the paper, we expanded from 32 transformer blocks to 48 blocks and then continued pre-training with the public dataset. Pre-training was performed for 3 days using 4 ml.g5.48xlarge instances from AWS (NVIDIA A10G GPU x 32ea). For pre-training, we used a sample set from Wikipedia.
+Similar to the methodology disclosed in the paper, we expanded from 32 transformer blocks to 48 blocks and then continued pre-training with the public dataset. Pre-training was performed for 3 days using 4 `ml.g5.48xlarge` instances from AWS (NVIDIA A10G GPU x 32ea). For pre-training, we used a sample set from Wikipedia.
 
 ### Fine-tuning
-After performing pre-training, instruction tuning and alignment tuning were performed sequentially. This process only took about 10 hours using ml.g5.24xlarge (NVIDIA A10G GPU x 4ea). The dataset used for instruction tuning is a sample set of the OpenOrca dataset, and the dataset used for alignment tuning is Intel's orca_dpo_pairs dataset.
+After performing pre-training, instruction tuning and alignment tuning were performed sequentially. This process only took about 10 hours using AWS `ml.g5.24xlarge` (NVIDIA A10G GPU x 4ea). The dataset used for instruction tuning is a sample set of the OpenOrca dataset, and the dataset used for alignment tuning is Intel's orca_dpo_pairs dataset.
 
 ### References
 - Base model: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)