The right model depends on the specific use-case. One should always robustly test available technology before committing to a solution. eg summarization of sci

The right model depends on the specific use-case. One should always robustly test available technology before committing to a solution. eg summarization of scientific articles, cooking blog posts, news articles, and racing events are all completely different use-cases. A fine-tuned GPT2 may or may not meet the performance of GPT4, but I would bet the out-of-the-box GPT4 performance beats GPT2. On the other hand, one also needs to consider the cost of GPT2 vs GPT4 against the quality-sensitivity of the particular use-case. The big deal about GPT4 is it does many novel tasks very well with a fairly cheap usage-based pricing model, zero infrastructure or fine-tuning required. Businesses like this.