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AnSungJae3489
Available on FriendliAI
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Model Details
Model Provider
AnSungJae3489
Model Tree
empero-ai/Qwythos-9B-Claude-Mythos-5-1M
Input Modalities
Output Modalities
Supported Functionality
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:
python
from unsloth import FastLanguageModelimport torchmodel, 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))
python
from unsloth import FastLanguageModelmodel, 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.)
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.