Fireworks Founder Lin Qiao on How Fast Inference and Small Models Will Benefit Businesses
Training Data
Firework’s CEO: Fine-tuning Is Not Simple At All
Different domains have varying requirements for fine-tuning AI models. In many fields like coding and OCR, off-the-shelf models perform exceptionally well without the need for customization. However, other areas necessitate tailored solutions as companies define success and quality based on their unique business logic. Tasks such as classification and summarization can greatly differ, necessitating fine-tuning to meet specific needs, such as templates in insurance. Despite the perception that fine-tuning may be straightforward, it is complex. Companies must first gather and label data, choose suitable fine-tuning algorithms—ranging from supervised tuning to preference-based methods—and decide between parameter-efficient approaches or full model fine-tuning. Furthermore, they might need to adjust hyperparameters for even better performance. This complexity poses significant challenges for app developers, particularly those new to AI. After fine-tuning, ongoing improvements are typically needed, requiring analysis of failure cases to determine whether to collect more data or adjust product design. Different contexts can label an outcome as a failure when it might simply reflect a design issue, such as the behavior of an AI when a user is inputting data in a table. To aid in this process, there is a push to simplify tuning by automating data collection, labeling, and selecting tuning algorithms while allowing companies to retain control over product design elements. Efforts are underway to streamline these features, with upcoming product announcements aimed at reducing the complexity faced by developers.
