Large language models (LLMs) have demonstrated state-of-the-art performance across multiple domains in various natural language tasks. Entailment tasks, however, are more difficult to achieve with a high-performance model. The task is to use safe natural language models to conclude biomedical clinical trial reports (CTRs). The Natural Language Inference for Clinical Trial Data (NLI4CT) task aims to define a given entailment and hypothesis based on CTRs. This paper aims to address the challenges of medical abbreviations and numerical data that can be logically inferred from one another due to acronyms, using different data pre-processing techniques to explain such data. This paper presents a model for NLI4CT SemEval 2024 task 2 that trains the data with DeBERTa, BioLink, BERT, GPT2, BioGPT, and Clinical BERT using the best training approaches, such as fine-tuning, prompt tuning, and contrastive learning. Furthermore, to validate these models, different experiments have been carried out. Our best system is built on an ensemble of different models with different training settings, which achieves an F1 score of 0.77, a faithfulness score of 0.76, and a consistency score of 0.75 and secures the sixth rank in the official leaderboard. In conclusion, this paper has addressed challenges in medical text analysis by exploring various NLP techniques, evaluating multiple advanced natural languagemodels(NLM) models and achieving good results with the ensemble model. Additionally, this project has contributed to the advancement of safe and effective NLMs for analysing complex medical data in CTRs.