Recent advancements in AI fine-tuning techniques, particularly the Low-Rank Adaptation (LoRA), are transforming how large language models (LLMs) are customized for specific tasks. LoRA significantly reduces the resource requirements for model updates, achieving a reduction of up to 10,000 times in updates and three times in resource demands. This approach allows for scalable and task-specific AI without the need for expensive infrastructure. The introduction of LoRA fine-tuning scripts for Mochi, an open video generation model with 10 billion parameters, further exemplifies this trend. Users can now personalize Mochi efficiently, with fine-tuning of a small portion of its parameters taking less than one hour. The community has expressed a strong demand for such fine-tuning capabilities, which enable the creation of consistent characters and specific effects with minimal resources.