It's a systemic issue going back decades. To me, it seems the Dutch government always wants to fix it with a hammer. Repeatedly. Discrimination increases, no REAL effort for integration is made (forcing people to take totally-not-racist "civic integration exams" is not an effort), and over the years the divide increases. Tell people they are monsters long enough, and that's what they'll become. But no one wants to hear that fixing it would take years or even decades of sustained effort and change. They just want it fixed. And fixed now.
There is no one magic bullet solution, unfortunately. And then it all comes to a head with the events in Amsterdam. The instigators need to be arrested and tried, but society needs to take a close look at what caused this to happen to begin with. And I doubt that will happen. Just more hammers.
LLMs are statistical word association machines. Or tokens more accurately. So if you tell it to not make mistakes, it'll likely weight the output towards having validation, checks, etc. It might still produce silly output saying no mistakes were made despite having bugs or logic errors. But LLMs are just a tool! So use them for what they're good at and can actually do, not what they themselves claim they can do lol.
Context was set to anywhere between 8k and 16k. It was responding in English properly, and then about halfway to 3/4s of the way through a response, it would start outputting tokens in either a foreign language (Russian/Chinese in the case of Qwen 2.5) or things that don't make sense (random code snippets, improperly formatted text). Sometimes the text was repeating as well. But I thought that might have been a template problem, because it seemed to be answering the question twice.
Super useful guide. However after playing around with TabbyAPI, the responses from models quickly become jibberish, usually halfway through or towards the end. I'm using exl2 models off of HuggingFace, with Q4, Q6, and FP16 cache. Any tips? Also, how do I control context length on a per-model basis? max_seq_len in config.json?
That would probably be a task for regular machine learning. Plus proper encryption shouldn't have a discernible pattern in the encrypted bytes. Just blobs of garbage.
Yes, that's about right. The answer is somewhere in between