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By the way, I usually use the AI for filtering, summarisation etc. Not for facts (like "what would be the best model of CPU I could buy" or "what is the capital
by wkat4242 1y ago
By the way, I usually use the AI for filtering, summarisation etc. Not for facts (like "what would be the best model of CPU I could buy" or "what is the capital of Estonia". When I do that I will link it to web searches anyway. Small models are indeed pretty useless for asking facts from. But big models can't be relied on either. They still hallucinate a lot. So I tend to combine it with web searches so I also have the references. But in general the whole fact thing for me is not a big usecase for AI.
- CjHuber 1y agoI understand your take, however my main use case is for the LLM to find subtle connections and biases in text or analyse nuances, so I figure more parms do make adifference in my case. Also in my experience it does also make a difference in summarization and filtering tasks, as a larger param one possibly knows better what is important and relevant in a text
- wkat4242 1y agoI can recommend to try a comparison if you can (like with OpenWebUI, you can simply make it run the same prompts through multiple models). If you have access to local models of course. Though you could also use some models like llama on groq. My usecases are different from yours of course but I don't really see significant difference in result quality in most cases.