Large language models have been increasingly effective in various NLP tasks, especially for machine translation task. However, these models require a lot of computational resources and need to be further fine-tuned on a specific training data to achieve better performance. Medium-sized language models often show significantly poor performance compared to the large language models. Therefore, it is necessary to study methods to solve this problem. In this paper, we propose a method to fine-tune a language model with a size of several billion parameters based on the instructions from a large language model such as GPT-4 through the contrastive learning technique, called CoPE - Contrastive Perturbation Enhancement for LLM-Based Machine Translation. Our proposal consists of three stages: fine-tuning the language model on a parallel dataset, generating entailment as positive and contradiction as negative examples from the training dataset based on a high-performance large language model such as GPT-4, and then using these examples to improve the model through the contrastive learning technique. These examples will be evaluated and ranked to increase the influence of quality examples. Experimental results show that our proposal with a base model of LLaMA-3.1 with 8B parameters achieves 35.99 bleu score, 85.28 COMET-22 score, and 88.90 XCOMET score on the WMT’21 and WMT’22 datasets. This result is competitive with models such as ALMA-13B-R trained based on the contrastive preference optimization technique and is higher than the GPT-3.5 model.