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AnSungJae3489/Kurken-9B-Summer-Merged
Kurken-9B-Summer-Merged is a machine learning model from AnSungJae3489. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for unsloth. The card lists the license as apache-2.0.
A LoRA finetune for fans of a certain alchemist who had one really good summer.
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From the Hugging Face model README
A LoRA finetune for fans of a certain alchemist who had one really good summer.
Yes, this is supposed to be a shitpost. A very high-quality shitpost.
Here's why it qualifies:
High-quality: the training methodology, data extraction, and model output. Shitpost: the premise, the price tag, and the timing.
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.
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.
~7,700 dialogue scenes (~844K tokens) extracted from multiple sources:
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.
Two ways to run:
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))
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:
All samples use temperature=0.9, top_p=0.92. No cherry-picking.
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!
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
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!
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.)
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.)
| Parameter | Value |
|---|---|
| Base model | Qwythos-9B-Claude-Mythos-5-1M (Qwen 3.5, 9B) |
| Method | QLoRA 4-bit |
| LoRA rank (r) | 32 |
| LoRA alpha | 64 |
| Trainable params | 58,195,968 (0.61%) |
| Effective batch size | 8 (2 x 4 accumulation) |
| Learning rate | 2e-4 (cosine schedule) |
| Epochs | 3 |
| Max sequence length | 2048 |
| Training time | ~1.2 hrs on consumer 24GB GPU |
| Peak GPU memory | ~12.8 GB |
| Logging | TensorBoard |
This is released in two forms:
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.
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.