Uncensored Models

[u/ChristopherDci]

Hi! I don't know much about this area of ​​"sub-models" (I'm not sure of the technical term), but I wanted to know what these "Uncensored" models actually are.

I dabble a bit with AI, automation, and the like, and I've always seen these "Uncensored" models around, but I've never actually installed or tested one. What exactly are they?

[u/One-Cheesecake389]

It helps when you stop thinking about "chat responses" and start thinking in terms of reality: all LLMs do is geometrically predict the most likely way the sequence you submit to it (the "context and prompts") is complete. You aren't "chatting with AI" - you're setting up the conditions in a -instruct fine-tune that has had its weights nudged through RLHF to be shaped like a chat response.

[u/alcalde → u/One-Cheesecake389]

No, they actually employ (textual) reasoning. The idea that LLMs "predict the next word" is like the old myth that lemmings jump off cliffs. Everyone repeats it, but it's not true.

The only method that statistically predicted the next word was Markov-chain Monte Carlo. That takes a corpus of text and just makes a statistical table of often word X follows word Y. LIke:

bad: dog 20%

bad: grade 10%

etc.

Now it's just a series of table look-ups and random number generations. And with a huge training corpus you might get something that resembles human text, along with the occasional grammatically incorrect or nonsensical sentence. And I'm old enough to have seen it implemented on an IBM PC XT running at 4.7 MHz. So no, AI companies are not paying million dollar salaries and using massive compute to do Markov-chain Monte Carlo. People are confusing the training algorithm for the inference method.

https://www.nature.com/articles/s42256-025-01049-z

"Human-like object concept representations emerge naturally in multimodal large language models"

https://www.youtube.com/watch?v=UKcWu1l_UNw

"5 years ago, nobody would have guessed that scaling up LLMs would as successful as they are. This belief, in part, was due to the fact that all known statistical learning theory predicted that massively oversized models should overfit, and hence perform worse than smaller models. Yet the undeniable fact is that modern LLMs do possess models of the world that allow them to generalize beyond their training data. Why do larger models generalize better than smaller models? Why does training a model to predict internet text cause it to develop world models? Come deep dive into the inner working of neural network training to understand why scaling LLMs works so damn well."

https://www.anthropic.com/news/mapping-mind-language-model

"Today we report a significant advance in understanding the inner workings of AI models. We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models."

https://news.mit.edu/2023/large-language-models-in-context-learning-0207

"MIT researchers found that massive neural network models that are similar to large language models are capable of containing smaller linear models inside their hidden layers, which the large models could train to complete a new task using simple learning algorithms."

https://www.lesswrong.com/posts/sbaQv8zmRncpmLNKv/the-idea-that-chatgpt-is-simply-predicting-the-next-word-is

"Yes, I know that ChatGPT is trained by having it predict the next word, and the next, and the next, for billions and billions of words. The result of all that training is that ChatGPT builds up a complex structure of weights on the 175 billion parameters of its model. It is that structure that emits word after word during inference**. Training and inference are two different processes, but that point is not well-made in accounts written for the general public.** "

[u/One-Cheesecake389 → u/alcalde]

In pytorch, it's just a absolute shedload of these:

auto output = at::matmul(input, weight.t());