In the context of increasing hate speech on social media platforms, this paper examines the effectiveness of various transformer-based models for detecting hate speech towards the LGBT+ community in Mexican Spanish. By focusing on tweets related to the LGBT+ community, we aim to identify the most effective model architecture for analyzing nuanced hate speech and complex language patterns. We compare the performance of pre-trained multilingual transformer models, including mBERT and XLM-RoBERTa, and address the challenges posed by class imbalance and linguistic diversity. Our findings demonstrate that DistilBERT, fine-tuned for Spanish, achieves the highest macro F1-score of 0.89, outperforming other models in accurately detecting hate speech. We also discuss strategies for handling data imbalance and provide an error analysis to highlight the limitations and potential biases of the models. Our research advocates for the deployment of these models to create safer online environments, enhancing user interaction and inclusivity across digital platforms.