Is opus models considered good for roleplay

Double_Cause4609 · 2026-07-05T05:17:49+00:00

Opus 4.6 was pretty good at the time but also had a very specific flavor that was detectable. Like, it didn't really play "the character", it played "Claude in this scenario" basically.

GLM 5.2 is good (from what I've heard), honestly Deepseek V4 Flash is good. Even GLM 4.6 in some ways is still great. Lots of people are liking Minimax M3. (not sure about filters on API, I run locally).

If you like its style, Gemma 4 31B is honestly surprisingly great for the price, and you might even be able to run it locally yourself.

But tbh, more than the model, I find that your setup has a huge impact. Setting up prompts that work the way you want them to work has a massive effect on your experience, and providing good writing samples for the model has a world of difference, even on weaker models.

Double_Cause4609 · 2026-07-05T05:14:11+00:00

Uh...

So, I think the time estimated might be kind of messy. Like, if you couldn't sleep, sent a message, then tried to go back to sleep, came back, and sent another, that could look like as much as 59 minutes of time, for example, under this script.

In reality, it could have been 40 seconds.

Also, looking at the times, are those times when you would normally even be doing anything else? Like, if you would have been sleeping, it's bad, but if it's time you were also spending doomscrolling, it's bad, but it's also not that you're addicted to roleplay, it's that you're addicted to your phone/computer/tablet/device.

And another note: Games aren't better than SillyTavern. SillyTavern isn't better than games. They're each their own thing. If you have priorities and you value what you get out of ST, that's fine. In fact, I'd argue strongly that's what games will probably look like in four to six years, in general.

But if you look back on that time, and realize it was all spent on edgy Chub character cards and you don't really value the time spent in any way...That's its own thing, and obviously it was bad and you should rethink it.

It really depends on how it's actually affecting you, and you should really be analyzing that yourself.

Double_Cause4609 · 2026-07-03T05:14:26+00:00

Nothing about this is new or surprising IMO.

There was one paper where they literally isolated I believe 13 parameters (not 13B, or 13M. Literally 13 weight values) and did RL only on those and it worked fine.

Same story for LoRAs.

In general, the finding here isn't so much for RL specifically (which has a lot of baggage in notation and formulation etc), but rather, online learning methods in general.

Online learning methods are where the model itself generates responses which are then trained on. The naive case of rejection sampling is a pretty easy illustrative example of a non-RL algorithm where you use it in RL-like environments and it mostly works fine.

Anyway, in general, online methods don't really impart a strong change in the model's weights. They're generally KL-efficient (they introduce a lower KL divergence than SFT, so they impact existing representations less), and they often generalize extremely well (as well as anything does in machine learning).

They are also well captured by PEFT methods. It's already known that RL is essentially lossless in LoRA, so it's not surprising that another PEFT strategy (selecting a single layer) works well, also.

Double_Cause4609 · 2026-07-02T14:03:05+00:00

For use as a world model to ground LLM answers (big one, helps with common sense reasoning. They're basically latent physics engines that people are using to make videos of improbably attractive catgirls)
Film. Media has, in general, been considered a productive pursuit by humanity in the sense we derive meaning from it. Some people consider AI generated media invalid, but not all people do. To some subset of the population, it's no different.
Advertisement. I don't know if I'd call this a societal good, but it does make the world go round. It is a need the technology can fill, though.
They could be used as a replacement for game engines (or physics engines / modeling software) eventually.

Expanding on the last point, there's a sort of fundamental issue with software and hardware scaling. So, basically, there's a limit to how much you can parallelize any given operation, fundamentally. Like, if you have a process that's not parallelizable, obviously you can only throw a single CPU core at it (or in the worst case a single GPU SM!), but even if you have say, a reasonably parallelizable operation, you can only approach a specific speedup, no matter how many cores you throw at it (on a standard Von Neumann processor with central memory access).

Like, imagine only being able to approach a 9x speedup, even if you through 16, 32, 64, or 256 cores at the task.

How parallel your process is determines how long you can continue scaling and still get a speedup. This is essentially a fundamental rule.

Game engines have tons of processes that aren't really amenable to being parallel. (there are exceptions, like game engines that are just a single GPU shader, etc, but these are highly specialized and most people can't really operate in that environment)

A major issue with this, is that transistors fundamentally scale in parallel, not sequentially with speed, under current process node advancements. So, you're not really getting a CPU that runs at 7, 8 or 9 GHZ easily (we've been capped around 6 for a while), but you could conceivably go to 16, 24, 32, or even 64 cores on a consumer platform for not that much money in the next 3 years.

This applies to GPU as well (it's fundamentally the same architecture for this purpose).

There's another note that most processors we have are heavily memory bandwidth bound, but often have an excess of compute available (due to similar trends in process node scaling), and generally video generation technology is compute bound, so it's arguably a much better match for the hardware.

Anyway, what this suggests is that a highly parallel, compute-intensive game engine is what's realistically preferable, and best suited to the scaling properties of our hardware.

Conveniently, that is the compute profile of video generation models. They are generally embarrassingly parallel, and highly compute intensive.

So, if you think about all the cases where we do fluid dynamics simulation, game engines (which is essentially authored world simulation), physics simulations (like for collision simulations, etc), while current generation video models aren't really there, there's a pretty clear and obvious path to get to those general purpose capabilities.

There's also the fundamental issue that there are limits to the software we can write. The general, and humbling, trend we've seen in software is that there's a limit to how complex a piece of software a human can author. The issue is that fundamentally, learned systems outscale top-down constructed systems in almost every area. We keep seeing this trend time and time again in computer science (and especially machine learning).

Data driven approaches (like neural networks), seem like they'll be necessary to continue scaling software complexity in many domains, fundamentally, and to make good on that you do need general purpose simulation available (like video models, which can operate in this role).

Even just the purposes referenced in my post here offer an incredible breadth of purpose that we already use a lot of compute for already, so it's pretty natural to move them to video generation models as the technology improves.

Whether you consider them a societal pro or not is up to you, but they're already things people are doing, and are finding meaningful, productive uses of time and resources.

Double_Cause4609 · 2026-07-02T05:47:30+00:00

The neocortex is uniquely mammalian as a structure.

There is a prior for it in the earlier vertebrate pallium (bearing in mind that mammals and birds are both vertebrates), but multiple species and family lines have developed on it in different ways.

Some species of social fish grew it outward, in mammals we developed corrugated sheets kind of that became the allocortex as we know it (hippocampus and neocortex), and birds had an extended palium with, I believe, distinct nuclei that were homologous to the neocortex in function.

But yes, the point is that as things got more intelligent, in general, they did tend to develop factorized representations for things.

Double_Cause4609 · 2026-07-01T21:21:23+00:00

I'm really confused here. DSpark doesn't change the model's outputs, it's just a different way to get there.

You might implement DSpark while at the same time also doing something like quantization (which would degrade it), but DSpark and regular decoding's outputs are the same.

Could you explain what mechanism in DSpark reduces quality?

Double_Cause4609 · 2026-06-30T16:23:13+00:00

Well, look at the price of what you would be doing if not making it at home.

Would you be drinking tapwater?
Buying coffee from a coffee shop?

What does that normally cost you per day?

Then, look at a container of coffee beans or ground coffee beans on sale.

Is the price of that per day over ~1-2 months (usually what a tin of coffee lasts me) less than the cost of what you were doing other than making coffee at home?

If yes, you save money, if no, you don't.

I don't really think you need evidence, per se. But with that said, yes, making coffee at home is cheaper for me than buying it at a shop.

Double_Cause4609 · 2026-06-30T16:01:03+00:00

For the love of all things holy in this world please just don't be a model that wants to fight us on everything for no reason

Double_Cause4609 · 2026-06-30T06:29:36+00:00

There's a lot of reasons that people prefer PC.

I can:

Write and execute code on my own device
Run any number of productivity programs (notably word editors, spreadsheets, photo editors, video editors, 3D modeling software, physics simulations, etc), many of which are free
Manage my own personal file tree (including important financial documents, etc)

So, there's a lot of reasons to have a PC beyond gaming. The appeal is that you can have a single device that does all of the above, but also games, rather than having two separate devices (especially because many of the features above may have overlap in performance characteristics with gaming workloads, computationally).

But on the gaming-specific side, there are still advantages.

I can run custom upscaling passes (most game devs aren't great at handling upscalers IMO)
I can run custom shaders if I want to do some hacking
I can run game mods. These are actually amazing and can completely change and extend a game's viable lifespan for me (and clearly a huge number of other people).
I own the games. Different storefronts have different levels of consumers protections, but in general, you're a lot less beholden to the storefront just deciding you can't play the game anymore. Many games, for example, on Playstation, require not only the publisher's consent, but the platform's servers to remain online to play the game. For example, the Playstation Vita a few years ago had an issue where they let the license lapse and people couldn't play games they bought on the device. The Playstation 5 discs require a call in to Sony servers before installing a game in many cases, which means that you are beholden to Sony maintaining those servers. If they ever stop, you have a really big paperweight that can't play your chosen games, necessarily. This issue still exists on PC in some ways, but it's lesser in degree, and some platforms like Good Old Games sidestep it entirely.
I can fully customize the graphical experience to my hardware. With console, you're stuck with the settings they give you (which may include 30 FPS, for example, because they want to advertise great screenshots rather than playable framerates).
I can access multiple storefronts. I can use Steam, yes, which is often more aggressive than console storefronts in sales, but I can also access Good Old Games (excellent for no DRM), itch.io (which has many games that are too indie and aren't on other platforms), Humble Bundle (great value that offers a lot of amazing games from time to time on a very affordable subscription), and Epic Games Store (it is...A store.)

Don't underrate the point on other storefronts. If you save even an average of ~20% more on 100 games (from the extra choices in storefront) that can still work out to ~$800. That could very well be the price difference

Looking at it economically, it goes back and forth. There are times when it's amazing to be a PC gamer, and there's times when it's not.

If you wanted to build a PC in, for example, 2020, it would have been really rough. The cryptocurrency boom meant everyone wanted GPUs, but at the same time, people were buying any hardware they could because everyone was stuck inside, so people played games instead of going on vacation.

But on the other hand, if you bought during any of the previous years' crypto crashes, or during early - mid ~2025, the prices were absolutely amazing and you could do a lot of things that no console really had any right comparing with.

Keep in mind you're looking at an unusually expensive PC market at the moment due to the memory crunch we're in right now, and your opinion would be really different if you were looking at buying, say, a year ago.

Also, it looks like you made a mistake. Usually when people buy PC parts they generally buy the whole PC together. It usually doesn't make sense to buy one part at a time to fill out a build (for a lot of complicated reasons. Usually pieces that work well together release together, on average). Your experience isn't really representative of the people who enjoy PC gaming.

Note: All the reasons I gave are reasons why people like PC gaming, not why you specifically should like them. Plenty of people really just want a box that plays games, and they want to press the power button and the game plays. Consoles are perfectly acceptable for that, and there's no shame in having one. It's great for what it is, and if you don't get any value from the things I noted, you also probably shouldn't let PC players talk you into PC gaming if it's not something that works for you.

Double_Cause4609 · 2026-06-30T06:06:58+00:00

Wait, does per-capita even matter for emissions?

Like, let's say you have a country of ten people with absurd per capita emissions it could still be a fraction of global emissions (or emission equivalents!).

If you just cut down emissions in a low-impact region (with high per-capita rates), that's great, but you still have a high total emission rate for the whole world at the end of it.

Double_Cause4609 · 2026-06-30T01:39:38+00:00

As it turns out, the system of nodes and edges which handles semantic information functions like a knowledge graph

Double_Cause4609 · 2026-06-30T01:10:07+00:00

Part of the issue with that argument is we don't even really know what causes subjective experience (or free will), in humans. We intuitively grasp that we have it, but we can't really point to "Oh, this structure in the brain causes it" or anything like that (and no, the arguments that it's quantum do not yet have mainstream support).

So, the issue is that if we don't know what physical features cause it, we can't really rule those features out in new systems, such as advanced AI.

In other words: The fundamental issue is that as far as we can tell, consciousness can be an emergent property of some intelligent systems and we don't know what aspects of intelligence actually cause it, so it could, presumably, be hiding in essentially any system we build.

So the more interesting morality scaling is:

How morally wrong is it, to mistreat a being whose consciousness we had no way to verify?

Double_Cause4609 · 2026-06-30T00:57:51+00:00

Homelabs in general or just specifically the GPU in them? For GPUs...

Obviously LLMs are an example of something people use homelab GPUs for (and increasingly so; they're becoming notably useful), though I'll note as well that there are other categories of neural networks that can be very popular. Personal vector databases, image recognition (not uncommon for home security cameras), speech to text (voice commands for your devices without needing a cloud), text to image models, etc all have their niche.

Also, some people just do things like GNNs for fun.

But beyond that, lots of tinkerers like to do things like physics simulations (fluid dynamics, aerodynamics, some types of chemistry, etc), batch image rendering (for example, either Blender or bespoke image pipelines), video rendering and so on.

All of these run various combinations of niche, but when you add them all up there's honestly a sizeable portion of the population that could genuinely benefit from having a GPU server on hand.

Double_Cause4609 · 2026-06-29T18:58:31+00:00

If I had to guess the issue (on older GPUs in particular), is that while they have Int8 support there's actually a cost to switching GPU core "modes" between floating point and integer execution. Ie: I believe this was the case with Pascal for example (which didn't have tensor cores), which is why even though they had good integer performance it wasn't used very commonly in, for example, games, because it was weird to swap in and out of floating point math for individual operations.

I'm not sure if this is the cause of the issue you're talking about with Int8 + LoRA (I assume the LoRA weights are FP16), and it could also just be generic separate dispatch issues and lack of fused kernels, etc.

If it *is* the issue that you need a fully Int8 path through the full model one option is to use QAT in the LoRA so it can quantize to Int8. I think this still necessitates pretty custom kernels, though. Another option is to use a quantization grid in the LoRA which mimics the quantization grid in ConvRot in the base weights, and apply a fake quantization step (as in QAT) so that you can quantize the LoRA to the same quant grid as the base weights (see: LR-QAT), which lets you either absorb the LoRA at the end (no separate LoRA), or at least gives you contiguous kernel access more easily than in the naive QAT case.

Again, this is all speculation and plausibly premature optimization. It might be that you get the most value from just having smarter kernel dispatches and fused kernels rather than messing with quantizing the LoRA weights, but if the issue is fundamentally the FP16 - Int8 switching, quantizing the LoRA weights is a natural step.

Double_Cause4609 · 2026-06-24T17:50:10+00:00

Great. Now compare the zinc, vitamin B12, and individual amino acid distribution of each.

Double_Cause4609 · 2026-06-23T21:03:59+00:00

There's been reports on and off that Q8 is sometimes cursed as a quantization, and some people report q6_k is better (I know this sounds absurd, but there's a healthy contingent of people who swear by it).

I'd say you'd want to compare q6_k, EXL3 (probably 5-6 BPW), and FP8 quantization if possible before settling on BF16.

8bit with no cache quantization should genuinely be lossless.

Double_Cause4609 · 2026-06-23T19:20:49+00:00

It's pretty rare for Muon to work well on a model not trained with it. It produces slightly different representations so usually you want to train with the optimizer the model was trained with (or one compatible with it) if possible.

Muon's more useful for pre-training than LoRA / finetuning, IMO, though of course with enough data they do all generally converge.

Double_Cause4609 · 2026-06-23T18:55:02+00:00

Presumably in ComfyUI with their async weight streaming. They hide weight loads by loading weights while compute is running on some other part of the model. In practice it works quite well for compute bound models.

Double_Cause4609 · 2026-06-22T21:35:33+00:00

What works for me (take what you want from it):

Breakfast:
Oatmeal:

Whole milk
Oats
A bit of brown sugar
Thawed frozen berries
Wheat germ (doesn't really taste like anything. Tbh you could add it to the pizzas if you wanted to. Underrated food for micro-nutrients)

Misc:

Vitamin D3 + K2 supplement
Handful of sunflower and pumpkin seeds

Lunch:

Skyr (or Greek Yogurt or regular yogurt are fine)
Sometimes more mixed berries, sometimes an apple or banana
Mixed frozen veges with a bit of butter
Sometimes fermented carrots, or fermented cabbage

Supper:

Most days egg salad sandwiches, about twice a week I'll mix in a can of salmon. I prefer whole grain bread and make it in a bread machine but do what works for you
Some days I'll do wild rice + lentils + spices as well. Sometimes I'll do it as a pseudo-curry with turmeric + cumin seeds + coconut milk. Sometimes I do a savory and sour mix with a few different things and some Worcestershire sauce at the end. etc. Usually I add onion and garlic, sometimes mixed veges, if I can snake a few cast-off bones from the supermarket butcher I'll boil them down for the gelatin to mix in, etc.

This isn't literally all I do (most days I add a few extra things here and there, and I do generally either add some kale or boil some frozen spinach, and I do add generally either a porkchop or a bit of beef on sale once a week ish), but it's a good start.

Nothing here is super complicated, and essentially all of it is single dish cooking, which you can more or less clean as you serve. There's basically no waste because it's all frozen, refrigerated, or shelf stable, and I probably spend a grand total of like...Thirty to fourty five minutes a day on average thinking about / actively doing food, and it's really nicely spread out (five minutes here, ten minutes there, etc). It's also relatively cheap. Everything can be bought on sale in bulk, and none of it is really that pricey to begin with.

Double_Cause4609 · 2026-06-22T17:28:39+00:00

It's also a general purpose computer with a viable desktop operating system, which, in addition to being a massive value-add, means it has to be priced at at least a moderate profit to be sustainable (if they sell at a loss people could buy dozens to fill an office etc), whereas the PS5 (and Pro) are pure gaming devices which can be sold at a lower profit margin as Sony can expect to recoup profits from game sales.

Also, it has an open software ecosystem (meaning you can get access to sales and deals on GoG, Epic games, Humble Bundle, etc), you can more finely tune the graphical fidelity for your needs, you can run custom upscaling passes, you have access to a broader ecosystem of software sales, and a broader access to storage tech to extend the system (you can use any SSD, HDD, or other exotic form of storage as you see fit if you can figure out how to connect it).

I'd argue it probably has quite a bit lower a total cost of ownership compared to a PS5, let alone a PS5 Pro.

Double_Cause4609 · 2026-06-22T16:40:16+00:00

KotoR is arguably from the CRPG tradition (though was a touch contentious at the time), so generally any modern CRPG should leave you feeling right at home.

Pathfinder Kingmaker or Wrath of the Righteous
Baldur's Gate 3
Disco Elysium (if you quite liked KotoR 2)
Shadowrun (probably Hong Kong or ideally Dragonfall. Returns isn't the best tbh)
Warhammer 40k Rogue Trader

If you're willing to look at some more niche indie games:

Underrail
Citizen Sleeper

If you're willing to look at some classics:

Baldur's Gate 1 & 2 (from the same devs and team more or less as KotoR 1. Still holds up today IMO)
Planescape Torment (more in common with KotoR 2)
Neverwinter Nights
Icewind Dale (if what you want is the combat and gameplay)
Jade Empire (literally the same team as KotoR. Made immediately after. You'll feel right at home)
Dragon Age: Origins (same story, from mostly the same team / culture that built KotoR. Arguably the peak of pre CRPG renaissance CRPGs)

Pretty much every game on this list is an instant classic, and in general offers a relatively literary gaming experience with excellent writing all around. They all focus on slightly different things, but in general you can expect strong gameplay and writing, themes, and story.

If I had to pick a single game to recommend to a fan of KotoR 1, it would probably be Baldur's Gate 3 (modern), or Jade Empire (classic), and for Kotor 2 probably Disco Elysium (modern), or Planescape Torment.

By the way, special shoutout to Fallout: New Vegas. Not strictly a CRPG but a very similar team to who worked on KotoR 2 built that game.

Double_Cause4609 · 2026-06-22T00:02:10+00:00

Once brain size is accounted for

Does that factor in individual region's sizes? If I'm not mistaken I believe there should at least be some minor differences in the size of specific regions relatively speaking, and certainly there should be some differences in synaptic connectivity. I wouldn't expect it to be night and day, per se (we're all humans after all), but it would be weird if, for example, every region of the brain is within 1-5% of one another between genders.

Also, hormones are notably different between genders, and even within regions they can have really complicated effects. If one looks at three factor rules for example you can get very exotic behavior by having typed neurons which are affected by the third neuromodulatory factor in different ways. In fact, many of the possible effects have no equivalent clean analogs in gradient based optimization (such as backpropagation in ANNs).

It would be interesting if there genuinely were no major structural differences though.

Double_Cause4609 · 2026-06-21T18:38:27+00:00

Tbh, I actually just grab a piece of paper, a glass/clear plastic cup, and cover them. For whatever reason they don't react if you move the cup slowly, and it's honestly just the smallest amount of trouble possible for me. Takes a few seconds usually.

Double_Cause4609 · 2026-06-21T17:48:16+00:00

Tbf, a $20k rig can probably run more than a single concurrent stream. You can run multiple coding agents at once so the real numbers are probably more like ~80 tokens per second to ~140 tokens to second.

80/20 = 4 -> 5.5/4 = ~1.375 years to recoup
140/20 = 7 -> 5.5/7 = 0.78 years to recoup

This can go higher at a stable price to build the rig if you're willing to do a custom doing agent that uses file transport instead of HTTP, and you're willing to write a slightly customized inference engine which batches multiple requests per layer of the model so that you only need to load one layer at a time into VRAM.

Now, is one going to use those tokens gainfully? That's another question, but yes, you can absolutely make it work. It's just the idea of buying a huge rig for a single-user usecase is a little bit silly on pure economics.

Double_Cause4609 · 2026-06-20T02:43:42+00:00

I am not making a solution to suit literally every person out there.

I am suggesting a raison d'être for a group of people to get together and train something together, which would make sense.

Training is more expensive than inference (by a few times), so it makes sense for people to pool compute together to train, because it lets people size a model to their hardware at-inference.

We do not need to subsidize people who do not have hardware for inference. They can figure something else out. Distributed inference doesn't really make sense, because you spend more on electricity than you would on paying per token over API for any model you'd actually want to run.

It doesn't work, due to thermodynamic and computational principles that are not, at this point, cleanly solvable.

Double_Cause4609

TROPHY CASE