This study investigates the effectiveness of fine-tuning protein language models (pLMs) for various protein prediction tasks. The research compares three state-of-the-art pLMs—ESM2, ProtT5, and Ankh—on eight different tasks, finding that task-specific supervised fine-tuning generally improves downstream predictions. Parameter-efficient fine-tuning methods, such as LoRA, can achieve similar improvements with significantly fewer resources and faster training times, up to 4.5 times faster than full model fine-tuning. The results suggest that fine-tuning is beneficial, especially for tasks with limited data, such as predicting the fitness landscape of a single protein. The study provides notebooks for easy fine-tuning of all models used, facilitating adaptation to various prediction tasks, including per-protein and per-residue predictions. The paper highlights the success of pLMs in protein prediction tasks, often surpassing traditional methods based on multiple sequence alignments (MSAs). pLMs learn from large datasets without experimental annotations, extracting embeddings that can be used for diverse tasks like secondary structure prediction, membrane region identification, intrinsic disorder prediction, and protein-protein interaction prediction. The study shows that fine-tuning pLMs can enhance performance, particularly when combined with task-specific heads and parameter-efficient methods like LoRA. However, the study also notes that fine-tuning may not always improve performance, especially for tasks like secondary structure prediction, where pre-trained embeddings may already capture sufficient information. The paper emphasizes the importance of data diversity, model size, and dataset balance in determining the effectiveness of fine-tuning. It also discusses the computational efficiency of parameter-efficient methods, which are particularly useful for larger models, reducing training time and memory requirements. The study concludes that fine-tuning pLMs is a promising approach for improving protein prediction tasks, especially when combined with parameter-efficient methods. It recommends using LoRA for most models, as it provides good performance with lower computational costs. The paper also provides resources and guidelines for researchers to apply fine-tuning to their own tasks, emphasizing the importance of data preparation, model selection, and hyperparameter optimization. Overall, the study demonstrates that fine-tuning pLMs can significantly enhance prediction accuracy and efficiency, making them a valuable tool in protein science.This study investigates the effectiveness of fine-tuning protein language models (pLMs) for various protein prediction tasks. The research compares three state-of-the-art pLMs—ESM2, ProtT5, and Ankh—on eight different tasks, finding that task-specific supervised fine-tuning generally improves downstream predictions. Parameter-efficient fine-tuning methods, such as LoRA, can achieve similar improvements with significantly fewer resources and faster training times, up to 4.5 times faster than full model fine-tuning. The results suggest that fine-tuning is beneficial, especially for tasks with limited data, such as predicting the fitness landscape of a single protein. The study provides notebooks for easy fine-tuning of all models used, facilitating adaptation to various prediction tasks, including per-protein and per-residue predictions. The paper highlights the success of pLMs in protein prediction tasks, often surpassing traditional methods based on multiple sequence alignments (MSAs). pLMs learn from large datasets without experimental annotations, extracting embeddings that can be used for diverse tasks like secondary structure prediction, membrane region identification, intrinsic disorder prediction, and protein-protein interaction prediction. The study shows that fine-tuning pLMs can enhance performance, particularly when combined with task-specific heads and parameter-efficient methods like LoRA. However, the study also notes that fine-tuning may not always improve performance, especially for tasks like secondary structure prediction, where pre-trained embeddings may already capture sufficient information. The paper emphasizes the importance of data diversity, model size, and dataset balance in determining the effectiveness of fine-tuning. It also discusses the computational efficiency of parameter-efficient methods, which are particularly useful for larger models, reducing training time and memory requirements. The study concludes that fine-tuning pLMs is a promising approach for improving protein prediction tasks, especially when combined with parameter-efficient methods. It recommends using LoRA for most models, as it provides good performance with lower computational costs. The paper also provides resources and guidelines for researchers to apply fine-tuning to their own tasks, emphasizing the importance of data preparation, model selection, and hyperparameter optimization. Overall, the study demonstrates that fine-tuning pLMs can significantly enhance prediction accuracy and efficiency, making them a valuable tool in protein science.