$61 total budget. A single 10-pull in a certain official app costs $20. This entire model costs three 10-pulls. You can't even spark a banner character for what this model cost to train.
Trained on a GPU in someone's bedroom. No data center. No A100 cluster. No venture capital. Just a consumer card and some legally purchased games.
Beats a certain company's official app to market. Launch is August 18. This model card exists now. The adapter ships before pre-registration closes.
No stamina system. GPUs don't get tired. Talk as long as you want. The only limit is your electricity bill.
All real dialogue, zero synthetic filler. Every training line was written by an actual game scenario writer. No LLM-generated slop. No template scripts. The dataset is cleaner than most academic papers.
Custom data extraction pipeline built from scratch. Undocumented format. Reverse-engineered byte-by-byte. The extraction tooling alone is a bigger flex than the model.

High-quality: the training methodology, data extraction, and model output. Shitpost: the premise, the price tag, and the timing.

What is this?

A QLoRA adapter (rank 32, ~58M params) trained on dialogue data from several official sources. The base model is Qwythos-9B-Claude-Mythos-5-1M — a Qwen 3.5 architecture already tuned for creative roleplay. This LoRA layers character voice, domain knowledge, and conversational mannerisms on top.

It's a companion model. You talk. She responds. No stamina bar. No gacha currency. No skin shop.

Why this exists

On August 18, 2026, a certain company is launching an official AI chat RPG app. It looks genuinely impressive — voice acting, live 2D art, world map, quest system, the works. A lot of talent and care clearly went into it.

That said: if you just want to talk to a certain character, you shouldn't need stamina bars and in-game currency to do it. This model is that — just the conversation part, no gates.

Extracted dialogue from multiple sources (personally purchased), parsed undocumented binary formats by hand, mapped speaker identities through voice pattern analysis, trained on a single consumer GPU. Weekend project.

Total cost breakdown:

Item	Cost
Source material (legally purchased)	~$60
Electricity (consumer GPU x ~2 hours)	<$1
Total	~$61

Compare to the official app: assuming standard pricing, a single multi-pull costs roughly $20. One mediocre banner costs more than this entire model's training budget.

You don't need a company. You need a GPU, some scripts, and a weekend.

Training data

~7,700 dialogue scenes (~844K tokens) extracted from multiple sources:

Several mainline titles — ~2,500 scenes
Official mobile spinoff — ~4,700 scenes
Broadcast adaptation (12 episodes) — ~80 scenes
Reference knowledge (profiles, systems, locations) — ~450 entries

All data is original dialogue from official sources, licensed copies. No synthetic generation. No LLM rewrites. If a line is in this dataset, a real writer wrote it.

Loss masking: only the target character's lines contribute to training loss. Other speakers provide conversational context without being learned as voice.

Usage

Two ways to run:

LoRA adapter (232 MB)

python

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    "empero-ai/Qwythos-9B-Claude-Mythos-5-1M",
    max_seq_length=2048, load_in_4bit=True,
)
model.load_adapter("path/to/ryza-lora")
FastLanguageModel.for_inference(model)

messages = [
    {"role": "system", "content": "You are a young alchemist from an island. Summer. Your friend is here."},
    {"role": "user", "content": "素材集めに行かない？"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs, max_new_tokens=150, temperature=0.9, top_p=0.92,
        repetition_penalty=1.05, do_sample=True,
    )
print(tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))

Merged model (17.5 GB, ready to run)

python

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    "path/to/kurken-9b-summer-merged",
    max_seq_length=2048, load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
# Same generate() usage as above

Recommended settings:

Temperature: 0.8–1.0, Top-p: 0.9–0.95, Repetition penalty: 1.05
Max new tokens: 150–300 for natural exchanges
System prompt: keep it light, summer-themed, mention the island, mention alchemy

Sample outputs

All samples use temperature=0.9, top_p=0.92. No cherry-picking.

Casual invitation

User: 今日は絶好の採取日和だね！どこ行く？ Response: ふっふっふ、分かってるよ。あちこち探索して、いい素材探しに行こう！

Alchemy advice

User: 俺も錬金術やってみたいんだけど、最初は何から始めればいい？ Response: うーん、どう説明しよう… 素材を集めてレシピを見つけて調合する… あ、そうだな。まずは素材の採取から始めてみろ。素材が分かれば、その次に何をどう調合するか、分かってくるはずだ

Battle encounter

User: 魔物だ！どうする？ Response: うっ……どうしよう。一人で戦うのは無理だ待って！あたしにもできるから！錬金術を使えば倒せるはず。材料を集めに行っていたんだもの！よし、任せて！

Friendly teasing

User: ○○とは最近どう？仲いいよね、二人とも。 Response: えっと……二人ともって、□□と△△？うん、まあね。あの二人は小さい頃から一緒に遊んだりしてて、今の関係だからかな？

(Names redacted in the sample — the model uses actual character names. We've blanked them here to keep the model card lawyer-safe.)

Stupid question about appearance

User: その…脚、すごく綺麗だよね。何か秘訣あるの？ Response: よく聞かれることなのかな？まあ、確かに昔はそういうことも気にしたことがあるけど今はあんまりそんなこと考えてないかな

(Deflected. Didn't bite. She knows, she doesn't care, she has alchemy to do.)

What this is NOT

Not an official product. No affiliation with any game publisher or AI company.
Not a replacement for playing the actual source material. Play the games. They're good.
Not voice-cloned. No TTS. No art generation. Just text.
Not uncensored by design — the base model happens to be uncensored. The training data comes from official scripts (standard rating). What emerges is between you and the model.

Known quirks

Some source material uses raw internal speaker IDs. If she occasionally references someone by number, that's why. Fix pending.
Mobile-spinoff scenes are predominantly other characters talking about the protagonist rather than the protagonist speaking. Good domain knowledge, less good voice data.
The model has strong opinions about alchemy. You have been warned.

Hosting

This is released in two forms:

LoRA adapter only — tiny download, apply to Qwythos-9B-Claude-Mythos-5-1M yourself
Merged model — full weights, ready to run, no assembly required

Host it wherever you host LLMs. Flat-fee hosting exists. You don't need stamina to talk to an AI. That's not how GPUs work.

License

Apache 2.0. Base model is Apache 2.0. Training data extracted from personally-owned copies for research/transformative use. Do what you want with the weights.

Made with approx. $61, one consumer GPU, and genuine annoyance at stamina bars in AI chat apps.

AnSungJae3489/Kurken-9B-Summer

Kurken-9B-Summer