In real-world use cases, it seems more appropriate to use advanced models to generate suitable rule trees or regular expressions for processing HTML → Markdown, rather than directly using a smaller model to handle each HTML instance. The reasons for this approach include:
1. The quality of HTML → Markdown conversion results is easier to evaluate.
2. The HTML → Markdown process is essentially a more sophisticated form of copy-and-paste, where AI generates specific symbols (such as ##, *) rather than content.
3. Rule-based systems are significantly more cost-effective and faster than running an LLM, making them applicable to a wider range of scenarios.
These are just my assumptions and judgments. If you have practical experience, I'd welcome your insights.
Just did a quick test in the https://model.box playground, and it looks like the completion length is noticeably shorter than other models (e.g., gpt-4o). However, the response speed meets expectations..
1. The quality of HTML → Markdown conversion results is easier to evaluate.
2. The HTML → Markdown process is essentially a more sophisticated form of copy-and-paste, where AI generates specific symbols (such as ##, *) rather than content.
3. Rule-based systems are significantly more cost-effective and faster than running an LLM, making them applicable to a wider range of scenarios.
These are just my assumptions and judgments. If you have practical experience, I'd welcome your insights.