  This paper describes our approach to the SemEval-2024 safe biomedical Natural
Language Inference for Clinical Trials (NLI4CT) task, which concerns
classifying statements about Clinical Trial Reports (CTRs). We explored the
capabilities of Mistral-7B, a generalist open-source Large Language Model
(LLM). We developed a prompt for the NLI4CT task, and fine-tuned a quantized
version of the model using an augmented version of the training dataset. The
experimental results show that this approach can produce notable results in
terms of the macro F1-score, while having limitations in terms of faithfulness
and consistency. All the developed code is publicly available on a GitHub
repository
