Update README.md

README.md

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 ## Training Details
 
+- We train our model with PEFT.
+PEFT is a technique that does not tune all parameters of a model during fine-tuning, but only a small subset of parameters.
+By tuning only a few parameters while leaving others fixed, the model is less likely to suffer from catastrophic forgetting, where the model forgets previously learned tasks when it learns new ones.
+This significantly reduces computation and storage costs.
+  
 - 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. 
 Instruction tuning is learning in a supervised learning format that uses instructions and input data together as input and output data as a pair.
+In other words, instruction tuning involves fine-tuning a pre-trained model for a specific task or set of tasks, where the model is taught to follow specific instructions or guidelines.