VLM岗位面试，被摁在地上摩擦。。。

Core Viewpoint - The article discusses the advancements and applications of large models in autonomous driving, particularly focusing on the integration of multi-modal large models in the industry and their potential for future development [2][4][17]. Group 1: Interview Insights - The interview process for a position at Li Auto involved extensive discussions on large models, including their foundational concepts and practical applications in autonomous driving [2][4]. - The interviewer emphasized the importance of private dataset construction and data collection methods, highlighting that data remains the core of business models [4][6]. Group 2: Course Overview - A course on multi-modal large models is introduced, covering topics from general multi-modal models to fine-tuning techniques, ultimately focusing on end-to-end autonomous driving applications [5][9][11]. - The course structure includes chapters on the introduction to multi-modal large models, foundational modules, general models, fine-tuning techniques, and specific applications in autonomous driving [9][11][17]. Group 3: Technical Focus - The article outlines the technical aspects of multi-modal large models, including architecture, training paradigms, and the significance of fine-tuning techniques such as Adapter and LoRA [11][15]. - It highlights the application of these models in autonomous driving, referencing algorithms like DriveVLM, which is pivotal for Li Auto's end-to-end driving solutions [17][19]. Group 4: Career Development - The course also addresses career opportunities in the field, discussing potential employers, job directions, and the skills required for success in the industry [19][26]. - It emphasizes the importance of having a solid foundation in deep learning and model deployment, along with practical coding skills [27].