So I really like how you cover a very complex subject matter simply. There are many moving parts to an LLM and your explanation allows folks to better understand what is going on. Its also useful for reminding some of us that use and train LLMs why advanced toolsets like Windsurf get it wrong every now and then too.
Good question! The temperature controls how smooth the probability distribution is (high temperature => smooth distribution => wild predictions). And top-k limits sampling to the k most probably tokens (important especially for high temperatures so that it doesn't go totally rogue).
Concise, complete and easy to understand. Thanks!
I had no idea we could provide custom logit processors, awesome! Thanks a lot!
Really great video, thank you, I hope more like these are coming!
So I really like how you cover a very complex subject matter simply. There are many moving parts to an LLM and your explanation allows folks to better understand what is going on. Its also useful for reminding some of us that use and train LLMs why advanced toolsets like Windsurf get it wrong every now and then too.
Great video!
So cleaningly explained! How do these concepts tie into the common API parameters for OpenAI e.g. temperature, top_k etc?
Good question! The temperature controls how smooth the probability distribution is (high temperature => smooth distribution => wild predictions). And top-k limits sampling to the k most probably tokens (important especially for high temperatures so that it doesn't go totally rogue).