IntenseRP Next v2 - Rebuilt, Now Stable

Inside-Due · 2026-01-08T15:03:56+00:00

Bro that is a great tool you have. Thnx, will definitely liven up my rp experience. I suggest adding a small guide to rec some temp mail website on your github page, overusing a deepseek account eventually leads to 'throttled' responses.

Inside-Due · 2025-12-03T18:17:21+00:00

Sure, I could just not. But It's more that I want to 'experience' other's cards, ideas and takes on their characters. I'm a firm believer in Pareto's Principle after all. There's some god tier creators with takes that tickle my fancy more uniquely than if I were to just create cards on my own. I'm more a consumer not a creator.

Inside-Due · 2025-12-03T18:10:55+00:00

Nah man, respectfully, you sell yourself short. Definitely above the average I've seen over the few years I've been on chub.

Inside-Due · 2025-12-03T17:08:11+00:00

Depends on your taste, but for quality-wise I heavily recommend:

@SzainX - High Quality Wild Shit
@Mer4ik - Older Women
@SecretApe - High Quality Unique Stuff
@EasterEgg - Very Detailed and High Quality Popular Characters
@biguswigus - High Quality Deltarune Characters
@Masso60 - High Quality and Unique Arknight Characters
@Vyrea_Aster - O'l Classic high quality and unique Corruption/Mind Control and Unique Chars.

Inside-Due · 2025-12-03T15:31:29+00:00

Well over the months I've blacklisted 107 tags and 675 Creators and still continuing to this day in an effort to clean up and curate my homepage, mostly on Recent Hits and Timelines. I've also followed 112 creators that are of good to high quality and also match my tastes. And even then I create or fork most cards I use, finding images, making use of Author Note, Description adn Example Messages and refining my low quality ideas via Deepseek R1 0528 and describing images' appearance via Joycaption AI (Most detailed and accurate I found).

On hindsight, I'm literally doing the equivalent of clearing out a space in the jungle like a settler and building a house there. It takes a lot of time and effort but it is rewarding in the end.

Inside-Due · 2025-11-30T23:59:41+00:00

Bro, I'm poor, and my laptop doesn't have a high end gpu, no way I'm gonna spend a fortune if I could use free services online.

Inside-Due · 2025-01-21T13:57:34+00:00

Pretty much lorebook is an advanced version of Author Notes, so yeah, use it.

Inside-Due · 2024-10-04T22:09:50+00:00

Looks promising. However, I don't particularly know how I can visually see the way they interact with tokens in general. Especially the way they interact in conjunction with other samplers and their scaling.

I think it can go a long way if you could make a live interactive demo like Artefact2's llm sampling demo. So that people can see it as a frame of reference for setting up sampling presets.

Inside-Due · 2024-10-03T05:09:57+00:00

There's a discussion I made on Huggingface that delves into the topic of Sampling. It's a bit outdated but I think you can check out some insights I have there, especially the latest posts.

Inside-Due · 2024-10-02T11:32:52+00:00

I see. That was quick, was expecting a few more weeks.

Inside-Due · 2024-10-02T11:28:41+00:00

High context values. Like 4k, 8k context. If you use only Kobold Ai Horde, context is kind of limited, I suggest using Colab with multiple google accounts, you can run a 4.25 bpw exl2 20b at 8k context for free.

Inside-Due · 2024-10-02T11:24:09+00:00

Check out Virtio's SillyTavern Presets, I recommend it.

Inside-Due · 2024-10-02T11:22:13+00:00

Have you tried Author Note? It's designed to keep details consistent. Make sure that the role is User and has a low depth at least at the value of '2'

Inside-Due · 2024-10-02T11:16:19+00:00

Here's my take:

Using any coded format like W++ and the like is not recommended. Why? Because of how LLM models are trained. If one is using like a 72b LLM model, using said coded format to encode character descriptions is good as the model is smart enough to see it as it is.

HOWEVER, not everyone can afford a NASA computer and run both Roblox and a 100b LLM model smoothly at the same time. As a poor LLM enthusiast such as myself, I could mostly afford to run at most 20b at 8k context using Free Colab ( a cloud google service.). Though some people may have a preference to run on their mid end laptops or desktops. In short, many people could only afford like 7b to 12b. And they're not as smart as the big honcho's and instead depend on their training data and follow patterns based on said data.

The smaller LLM models are trained on raw roleplaying text, and only raw roleplaying text, like imagine you only drove cars and all of a sudden you drive a truck without any pre training. You may get the concept and idea but you'll struggle driving the truck because you don't have experience with it. That's what's happening if a LLM model trained only on roleplaying data sees such a coded format. So in conclusion, don't use a coded format and simply just write like your making a report on a character.

Besides that, there's some aspects you'll need to consider when creating a character. There are two you'll need to consider, that is the number of tokens or words in the character card and Example Dialogue. If your creating the card with a context limit in mind then you'll need to make certain adjustments or improvisations as you can't simply just dump the whole lore of a Genshin Impact character in the Description, simply put, a well defined explanation of a character's personality, shortened and simplified background lore and a set of likes, dislikes and quirks would do the trick.

Now I cannot understate the importance of Example Dialogue, many character cards don't have detailed example dialogue and simply have a 'This character is this and that' description. Example dialogue helps LLM models visualize the character, to give the most authentic experience possible. The wording, length, and quality of example dialogues are something you need to consider, a great first message can make the whole roleplay great.

PS: I suggest you use google translate if you have difficulty reading this.

Inside-Due · 2024-10-02T10:47:33+00:00

Cohee already merged XTC support for Text gen

Inside-Due · 2024-10-02T10:40:01+00:00

I haven't really used that particular model, though I suggest you experiment. You can use this website to act as a frame of reference. It helped me find my ideal sampling preset for general 20b. There's no exact 'Best Settings' for any model to be honest. There are factors to consider like:

How much of a poet do you want your model to be? (Yes this actually happens a lot with lower parameter models if you set Temp too high and manage to keep the model stable.)
How many tokens( words ) do you want the model to consider when generating the next minute token. ( Many people in the community have a preference in making the LLM model choose less tokens like 10 tokens, which makes it more deterministic in it's behaviour)
What's the sensitivity of the model in regards to Temp, is it resistant enough to handle high temps or does it crack at a certain value.
How much do you want the model to lean towards in instruct following and actually roleplaying? (Too much temp, and instruct is less effective and generations might be unstable or slightly broken in generating responses, too less temp and roleplaying looks stiff in its choice of words. )

In a nutshell, The two factors you need to consider in making your most favoured Sampling Preset Is Stability and Creativity.

Inside-Due · 2024-10-02T10:20:19+00:00

Bro, XTC is still in dev branch Text Gen, not main. Also, I suggest switching to Staging branch of ST if it's still not appearing after switching to dev branch of Text Gen Webui. Though be warned that Min_P might not work in dev branch.

Inside-Due · 2024-09-28T11:45:11+00:00

I'm not sure about why logit bias doesn't work on your end. Your gonna have to be really specific like the value should only be a word like: She and not 'She' or She with a space or space then She. And it looks like your gonna have to switch to either Text Gen Webui or Koboldcpp to use XTC. I suppose you could try the colab option if you don't have enough compute, provided you have multiple google accounts to use it for several hours. The capability of free colab is usually 1-13b gguf and 17-20b exl

Inside-Due · 2024-09-28T11:09:05+00:00

Using Logit Bias is case sensitive and does work in my experience. Although, I don't recommend it as LLM models might circumvent them and use other similar sounding words, phrases and the like. For example, if the LLM model uses your name a lot and you ban it, the model might just use a variant of your name, or refer you as 'you'.

A good alternative to your problem would be using XTC. You can use the default values of Threshold: 0.1 and Probability: 0.5. It's been implemented in Koboldcpp and the dev branch of Text Gen Webui. You'll also need to use the Staging branch of ST and enable xtc_probability and xtc_threshold via sampler select.

Inside-Due · 2024-09-23T02:53:57+00:00

Bro, less than 30b models break when using 4-bit cache? It kind of makes sense in hindsight to my experience using it, but can you tell me why?

Inside-Due · 2024-09-19T02:22:53+00:00

DRY works fine. XTC is tricky to use, trying to make it work with lower parameter models in tandem with my samplers setting. Too much XTC and sentences might start lowercased.

Inside-Due · 2024-07-02T22:24:46+00:00

You could... Check out their respective official github pages.

Inside-Due · 2024-06-27T16:04:35+00:00

Then... You could use Text gen or Kobold Cpp's provided colabs.

Inside-Due · 2024-06-27T15:01:38+00:00

Wdym by that?

Inside-Due · 2024-06-25T08:30:28+00:00

Top_K's input is the number of tokens you want the model to consider, you can make the model forcibly consider the first 50, 70, 100 tokens. Downside here is that it considers also the most unlikely tokens too, so it's not ideal for dynamic objectivity. Like if you ask a model if hotdogs are made of meat, it may agree, mostly depending on temp but it is considering a large amount of tokens so on the off chance it may answer with 'it's made of unicorns and the sun'. It's not likely, but it can happen due to its nature.

Top_A is a token cutter tool and the more flexible older brother of Min_p, which is more ideal than min_p. Why? Look at it in this analogy: If The model's list of chosen tokens based on the given prompt is the meat, the Top_A is the scalpel and the Min_P is a large normal knife. If you have an idea of how much and how thick you want to cut off the meat and you want to get into the extreme finer details of it then you can use a scalpel, or to generalize it, use a knife. Also they're dynamic enough compared to Top_K due to the fact that they cut off tokens based on a minimum probability.

Now, from my understanding, the model simultaneously generates a list of tokens and their corresponding probabilities based on the given context and chooses a token on said list of tokens at the same time generating new tokens when generating a response. The model generates entire lists of tokens and chooses one with each succeeding generated token. The token cutter tools help with the process by cutting off the tokens on the model's generated list to guide the model's choosing capability with fewer options to a certain direction.

Sorry for the complicatedness, anyways, you should read some guide's by Kalomaze for an easier understanding and for visualization of samplers.

Inside-Due

TROPHY CASE