openmic.social is an uncensored community. You may encounter strong language, controversial opinions, and mature or NSFW material. You must be 18+ to browse. Illegal content is prohibited and removed on sight — please report it. By continuing, you accept that you may see content you personally disagree with.
I’m not aware of any realistic way to vet published model weights like that.
It works for some open-source software, because you can download the source and compile it yourself. But most of the “open” models don’t publish their entire training set, or enough details on the training for it to be truly reproducible. DeepSeek doesn’t publish their data sets, and they probably couldn’t, legally, since they “collaborate with third-party data providers to obtain proprietary datasets”. And even if they did, it would be prohibitively expensive to train it on your own, unlike simply compiling open-source code.
The actual code for training LLMs is relatively trivial. The secret sauce is the training data and fine-tuning.
You have agents classify what each portion of the network responds to and how. One compares it to filling out a very large crossword puzzle: https://already-clean.goodfire.com/
That’s the theory anways, not sure if it will stand the test of time…
Sounds interesting, thanks for the link. It’s a little over my head tbh but I’ll definitely take a closer look.