As implied above, the raw format fed to/outputed from Deepseek R1 is:
<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><think>The model rambles on to itself here, “thinking” before answering</think>The actual answer goes here.
It’s not a secret architecture, theres no window into its internal state ehre. Thi is just a regular model trained to give internal monologues before the “real” answer.
The point I’m making is that the monologue is totally dependent on the system prompt, the user prompt, and honestly, a “randomness” factor. Its not actually a good window into the LLM’s internal “thinking,” you’d want to look at specific tests and logit spreads for that.
Shared to an IM group from somewhere and reshared here. What I understood is it seems to be pretty open when asked how it comes to its answers
As implied above, the raw format fed to/outputed from Deepseek R1 is:
It’s not a secret architecture, theres no window into its internal state ehre. Thi is just a regular model trained to give internal monologues before the “real” answer.
The point I’m making is that the monologue is totally dependent on the system prompt, the user prompt, and honestly, a “randomness” factor. Its not actually a good window into the LLM’s internal “thinking,” you’d want to look at specific tests and logit spreads for that.