Pretty sure I saw an uncensored version yesterday on localllama

I found DeepSeek R1 (better for questions) and V3 (better for prose) to be very willing to discuss sex with a simple system prompt, as well as being very pleasant in articulation. I guess, I prefer them because they are almost SOTA and very large.

Not through the official interface though. Needs to be hosted by a third party. OpenRouter has a generous free tier for both.

I just saw that there is an abliterated version as well. Not sure how to try it though.

Sorry just to ask, what kind of job do you have? Sex therapist sounds like the closest?

I've been experimenting with using various LLMs as a game master for a Lovecraft-inspired role-playing game (not baked into an application, just text-based by prompting). While the LLMs can generate scenarios that fit the theme, they tend to be very generic. I've also noticed that the models are extremely susceptible to suggestion. For example, in one scenario, my investigator was in a bar, and when I commented to another patron, 'Hey, doesn't the barkeeper look a little strange?', the LLM immediately seized on that and turned the barkeeper into an evil, otherworldly creature. This behavior was consistent across all the models I tested. Maybe by prompting the LLM to fully plan the scenario in advance and then adhere to that plan would mitigate the behavior but I haven't tried it. It was just an experiment and I actually had a lot of fun with the behavior. Also the reactions of the LLM if the player does something really unexpected (e.g. "the investigator pulls a sausage out of his pocket and forcefully sticks it into the angry sailors mouth") are sometimes hillarious.

Are you getting good results with this? Some time ago I build an "overworld simulator" by having a bunch if jsons that represented villages, characters, building, story hooks and plots. I just asked ChatGPT to "simulate the gameworld by a week".

Technically this worked great, but everything was somewhat bland and generic.

Noteable not-highlights: - there is a shimmer in the nearby forests that keep villagers up at night -> it's an orc camp - there is a mysterious figure in town -> it's a Aragon type ranger

Where do you get started with something like this? Sounds like a fun project

Openrouter provides a multi-model, multi-provider API for hosted LLMs. I myself used their API to compare different LLMs for document classification.

They provide usage rankings [1] and the top 10 applications, in terms of tokens used, are:

1. AI coding agent 2. AI coding agent 3. AI coding agent 4. Library for calling LLMs 5. Role play chat 6. Role play chat 7. Role play chat 8. General purpose chat 9. AI coding agent 10. Role play chat

Certainly, the coding agents are burning through far more tokens. No doubt about that.

And there's undoubtedly a major bias introduced by the fact chatgpt and claude $20/month accounts are heavily discounted, so if your use is SFW why pay more to get an uncensored model?

But overall, to me, the evidence seems pretty robust.

[1] https://openrouter.ai/rankings

Not quite. The market for romance (which can, in fact, get arbitrary degrees of spicy) is by far the largest literary market. (Source: https://bookadreport.com/book-market-overview-authors-statis...)

LLMs are also hilariously bad at what makes erotica hot in the first place.

Not NSFW, but I was surprised at hearing 2 random 20-something adults tell me they use chatgpt to write fan fiction to read. Those 2 people didn't know each other but were from similar demographic. It was surprising nonetheless. I can't imagine why anyone would care about such a bizarre use case.

I suspect NSFW novels is a bigger market than you think. Spicy romantasy is popular among a certain set of readers.

The claim that Fifty Shades of Grey and, I don't know, Norman Mailer?, Chuck Tingle?, are less common on book shelves and night stands than cyberpunk paperbacks seems obviously wrong to me.

Perhaps you could elucidate further on this subject? I'm mostly into books from 1800-1985 or so and don't know much about contemporary literary fashion.

Edit: Jean M Auel was extremely common in occidental households a few decades ago, especially the first and second books about Ayla, I'd wager much, much more common than cyberpunk.

Same goes for books by Alex Comfort.

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There's something Freudian about the idea that the more you can customize porn, the more popular it is. That, despite the impression that "all men want one thing", it turns out that men all want very different and very oddly specific things. Imbuing somrthing with a "magical" quality that doesnt exist is the origin of the term "fetish". Its not about the raw attractive preference for a particular hair color; its a belief in the POWER of that hair color.

1, Porn. 2 Military.

even if it is victim free, it can affect mental health in a way that a consumer will be more compelled to do a criminal act and create a real victim.

let's say you publish a Steam game how to be a school shooter and shoot kids, wouldn't that lead to real school shootings ?

who can definitely say that computer generated content about criminal behavior, won't lead to real crime with real victims?

https://en.wikipedia.org/wiki/Active_Shooter

There's nothing wrong with it, but you have to understand the differences between different user groups to know which limitations are relevant to your own use cases. "It doesn't follow instructions" could mean "it won't pretend to be a horny elf" or "it hallucinates fields outside the JSON schema I specified"; the latter is much more of a problem for my uses.

    {
      "race": "elf",
      "horny": false
      ^^^^^^^^^^^^^^
      Unsupported value.

I have no problem with it and I can understand why people don't want to say "I'm trying to pornify this model and it refuses to talk dirty!" in public. But if you're calling a model garbage maybe you should be honest about what the "problem" is.

The pro-porn side has zero PR because respectable public figures don't see pro-porn advocacy as a good career move. At most, you'll get some oblique references to it.

Meanwhile, the anti-porn side has a formidable alliance:

Right-wing, religiously-motivated anti-porn activists. Left-wing, feminism-motivated anti-porn activists. Big corporate types with lots of $$$$ to spend who want their customer support chatbot to be completely SFW at all times. AI safety folk who think keeping the model on a tight leash is an ethical obligation, lest future iterations take over the world. AI vendors who are keen on the yes-it-might-take-over-the-world narrative. AI vendors who just don't want their developers having to handle NSFW stuff in work. Politicians who don't know a transformer from a diffusion model, but who've heard a chorus of worries about lost jobs and AI bias and deepfakes and revenge porn.

These people will speak up in public at the drop of a hat.

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I haven't tried your exact task, of course, but I've found a lot of success in using JSON structured output (in strict mode), and decomposing the response into more fields than you would otherwise think useful. And making those fields highly specific.

For example: make the suggestion output an object with multiple fields, naming one of them `concise_suggestion`. And make sure to take advantage of the `description` field.

For people not already using structured output, both OpenAI and Anthropic consoles have a pretty good JSON schema generator (give prompt, get schema). I'd suggest using one of those as a starting point.

Yes... Specifically by AI dungeon. He got dialog-tuning working (not without some hiccups) before anyone else, and it drew a lot of attention from the API provider.

But then people used it for erotic RP, and it became a PR disaster, and the author blamed his pervert customers. Never mind that certain characters who turned up a lot in AI dungeon stories turned out to be from a fantasy writing site the author had used for fine-tuning material (without permission of course) and he hadn't filtered OUT the dirty stories to put it like that.

Existing models are more than good enough.

The jargon to google here is "length bias"

It turns out if you generate two LLM responses and ask a judge to choose which is better, many judges have a bias in favour of long answers full of waffle.

> I swear they have tuned their models to waste tokens.

Which seems a bit weird, because the customers of the chat interface (ie non-API customers) don't pay per token.

Because it's naughty! US is.... prudish, to say the least.

Or imagine if Bill Gates decided to not release Comic Sans in the 90s cause someone could have written something offensive with it....

oh wait that wouldn't have been too bad

(/S)

Good point, though at the price of a 5090 I'm more tempted to get the 5090, as I do still game a bit as well.

Sadly no. I'd like to keep it untainted, but also because the tables involved are straight from my work, which is very much not OSS.

I can however try to paraphrase it so you get the gist of it.

The question asks to provide a SQL statement to update rows in table A based on related tables B and C, where table B is mentioned explicitly and C is implicit through the foreign keys provided in the context.

The key point all previous models I've tested has missed, is that the rows in A are many-to-one with B, and so the update should take this into account. This is implicit from the foreign key context and not mentioned directly in the question.

Think distributing pizza slices between a group of friends. All previous models has completely missed this part and just given each friend the whole pizza.

GPT-OSS correctly identified this issue and flagged it in the response, but also included a sensible assumption of evenly dividing the pizza.

I should note some of the previous models also missed the implicit connection to table C, and thus completely failed to do something sensible. But at least several of them figured this out. Of course I forgot to write that part down so can't say offhand which did what.

As for the code, for example I've coded a Y combinator in Delphi, using intentionally terse non-descriptive names, and asked the models to explain how the code works and what it does. Most ~7B models and larger of the past year or so have managed to explain it fairly well. However GPT-OSS was much more thorough and provider a much better explanation, showing a significantly better "understanding" of the code. It was also the first model smaller than LLama 3 70B that I've tried that correctly identified it as a Y combinator.

Here's a more concrete example where GPT-OSS 20B performed very well IMHO. I tested it against Gemma 3 12B, Phi 4 Reasoning 14B, Qwen 2.5-coder 14B.

The prompt is modeled as a part of an agent of sorts, and the "human" question is intentionally ill-posed to emulate people saying the wrong thing.

The prompt begins with asking the model to convert a question into matlab code, add any assumptions as comments at the start of the coder, or if it's not possible then output four hash marks followed by an reason why.

The (ill-posed) question is "What's the cutoff frequency for an LC circuit with R equals 500 ohm and C equals 10 nanofarrad?"

Gemma 3 took the bait and treated R as L and proceeded to calculate the cutoff frequency of an LC circuit[1], completely ignoring the resulting mismatch of units. It did not comment at all. Completely wrong answer.

Qwen 2.5-coder detected the ill-posed nature, but instead decided to substitute a dummy value for L before calculating the LC circuit answer. On the upside it did add the comments saying this, so acceptable in that regard.

Phi 4 Reasoning reasoned for about 3 minutes before deciding to assume the question is about an RC circuit. It added this as a comment, and correctly generated the code for an RC circuit. So good answer, but slow.

GPT-OSS reasoned for 14 seconds, and determined the question was ill posed, thus outputting the hash marks followed by The cutoff frequency of an LC circuit cannot be determined with only R and C provided; the inductance L is required. Good answer, and fast.

[1]: https://en.wikipedia.org/wiki/LC_circuit#Resonance_effect

> stories written by larger models trained on real-world data

I suspect there are no larger models trained on pure real-world data. They all use a mix of real and generated.

> Why would anything change?

It's not that facts change across time, but the relevancy of the details change. For example, it would be great if we could teach LLMs all the APIs all React versions have ever had, but if we do that for everything, there will be no limit to the weight's weight, and we'd need new weights every quarter if not more often. That seems very unsustainable.

So the information that corresponds to "What is the current React API for X" changes whenever the API changes, but "What is the React v5 API for X" remains the same. Having the model being able to look up those things via external channels would let us use the same models for way longer, if you need "up to date data" about things.

We don't feed the model all the text ever. They are still trained on less than 1% of the entire Internet corpus.

Why? You gotta provide what your customers want.

And it's far from sad that we have so many resources, we can give everyone a supercomputer in their pocket just to take selfies and talk about Pokemon. Why would our AIs be any different?

> By definition, a model can't "know" things that are not somewhere in its training set, unless it can use a tool to query external knowledge.

Well, it could also make inferences. Like, it could find a new mathematical proof, even if that's never in the training set.

> all known written text available

If phi5 is trained on synthetic data only then info on how to make drugs must be in the synthetic dataset.

I don't care if it's open source or not. I care if people call something open source which it isn't.

Do you care about binary blobs in the kernel? No. Are binary blobs in the kernel open source? No.

But it is tedious to go through the same discussion every 10 years, with a relentless industry that wants to dupe people.

If there wasn't a benefit in for them, they would not call it open source.

The license gives you the right to modify the weights, how you do the modification is up to you. The rest is in the realm of IP, know-how, etc. Apples and oranges.

You also need the training data, so you can ensure you're not benchmarking on the training set, fine-tuning on the training set (overfitting with extra steps), or otherwise breaking things.

It's not about the preferred way. Else open source software would need to give you their IDE setup, CI/CD setup, access to all internal tools, etc. Software like sqlite don't release their full test suite. They paywall the preferred way of making changes, yet they are open source.

>The “source code” for a work means the preferred form of the work for making modifications

The GPL refers to a form of the artifact being released

> Do the labs do not use source code?

The weights are part of the source code. When running inference on a model you use the architecture, config files and weights together. All of these are released. Weights are nothing but "hardcoded values". The way you reach those values is irrelevant in the license discussion.

Let's take a simple example: I write a chess program that is comprised of a source file with 10 "if" statements, a config file that matches between the variables used in the if statements and a "hardcoded values" file that stores the actual values. It would be a crappy chess program, but I hope you agree that I can release that as open source and no-one would bat an eye. You would also be granted the right to edit those hardcoded values, if you wish so. You'd perhaps make the chess bot better or worse. But you would be allowed to edit it, just like I would. That's the preferred way of modifying it. Me providing the methods that I used to reach those 10 hardcoded values has 0 bearing on my crappy chess bot being open source or not. Do we agree on that?

Now instead of 10 values, make it 100billion. Hey, that's an LLM!

> It is a bit like arguing that releasing a binary executable is releasing the source code.

That's the misconception. Weights are not a binary executable. In other words, there isn't another level above weights that the labs use to "compile" the weights. The weights exist from the beginning to the end, and the labs edit the weights if they want to modify the models. And so can you. There isn't a "compilation" step anywhere in the course of training a model.

> Consider the following: it is possible to release binaries under the Apache2 license. Microsoft has, at least at one point, released a binary under the BSD license. These binaries are not open source because they are not source.

Agreed. But weights are not binaries in the licensing context. For weights to be binaries it would imply another layer of abstraction, above weights, that the labs use as the preferred way of modifying the model, and then "compile" it into weights. That layer does not exist. When you train a model you start with the weights (randomly initialised, can be 0 can be 1, can be any value, whatever works best). But you start with the weights. And at every step of the training process you modify those weights. Not another layer, not another abstraction. The weights themselves.

You are free to look at every single weight and study how it affects the result. You can see how the model is architected. And you don't need training data to be provided to be able to modify the weights. Software can still be open source even if it isn't friendly to beginners.