We present PersianMCQ-Instruct, a comprehensive resource that includes a dataset and advanced models for generating multiple-choice questions (MCQs) in standard Iranian Persian, a low-resource language spoken by over 80 million people. This resource features three state-of-the-art models for Persian MCQ generation: PMCQ-Gemma2-9b, PMCQ-Llama3.1-8b, and PMCQ-Mistral-7B. Inspired by the Agent Instruct framework and GPT-4o, we created the dataset by curating over 4,000 unique Persian Wikipedia pages, resulting in three MCQs per page and a total of over 12,000 questions. To ensure the quality of this dataset, we conducted human evaluations and model fine-tuning, both of which demonstrated significant performance improvements in Persian MCQ generation. The dataset and models are publicly available, offering valuable tools for researchers and educators, with particular benefits for advancing Persian-language educational technology.

Pre-trained LLMs have demonstrated substantial capabilities across a range of conventional natural language processing (NLP) tasks, such as summarization and entity recognition. In this paper, we explore the application of LLMs in the generation of high-quality protein sequences. Specifically, we adopt a suite of pre-trained LLMs, including Mistral-7B, Llama-2-7B, Llama-3-8B, and gemma-7B, to produce valid protein sequences. All of these models are publicly available (https://github.com/KamyarZeinalipour/protein-design-LLMs).Unlike previous work in this field, our approach utilizes a relatively small dataset comprising 42,000 distinct human protein sequences. We retrain these models to process protein-related data, ensuring the generation of biologically feasible protein structures. Our findings demonstrate that even with limited data, the adapted models exhibit efficiency comparable to established protein-focused models such as ProGen varieties, ProtGPT2, and ProLLaMA, which were trained on millions of protein sequences. To validate and quantify the performance of our models, we conduct comparative analyses employing standard metrics such as pLDDT, RMSD, TM-score, and REU. Furthermore, we commit to making the trained versions of all four models publicly available, fostering greater transparency and collaboration in the field of computational biology.