Core Viewpoint - The article emphasizes the importance of individual approaches and methodologies in the field of large language models (LLMs), particularly in the context of fine-tuning and data quality, suggesting that the technical depth of work in this area is highly dependent on personal engagement and practices [5][16]. Data Work - Method 1 involves inheriting training data from colleagues without checking data quality, which may lead to suboptimal results [7]. - Method 2 suggests downloading open-source data to create a "system + query + answer" dataset [8]. - Method 3 focuses on generating data using GPT-4, emphasizing the diversity of prompts and the importance of data quality checks [8]. - Method 4 advocates using user interaction logs to drive data construction, analyzing user feedback to improve answer quality [9]. - Method 5 recommends breaking down complex tasks at the data level to enhance model performance [9]. Training Code - Method 1 involves inheriting training code and making minimal modifications [11]. - Method 2 encourages a thorough understanding of training code parameters and their implications [11]. - Method 3 promotes questioning and improving training code, such as optimizing speed and framework choices [12]. Experimental Analysis - Method 1 suggests running prepared evaluation sets and addressing data quality issues based on results [14]. - Method 2 involves analyzing bad cases from models to identify underlying issues and designing experiments to validate findings [14]. - Method 3 emphasizes the relationship between model results, data quality, and training methods, advocating for a comprehensive analysis of training logs and evaluation results [15]. Community and Collaboration - The article highlights the establishment of a large community focused on various aspects of autonomous driving technology, including large models and multi-sensor fusion, with nearly 4,000 members and over 300 companies and research institutions involved [18].