Instructions to use hwihwalab/dama-kurong-solopreneur-ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hwihwalab/dama-kurong-solopreneur-ko with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="hwihwalab/dama-kurong-solopreneur-ko")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hwihwalab/dama-kurong-solopreneur-ko")
model = AutoModelForCausalLM.from_pretrained("hwihwalab/dama-kurong-solopreneur-ko", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hwihwalab/dama-kurong-solopreneur-ko with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M

Use Docker

docker model run hf.co/hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M

LM Studio
Jan

vLLM

How to use hwihwalab/dama-kurong-solopreneur-ko with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hwihwalab/dama-kurong-solopreneur-ko"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hwihwalab/dama-kurong-solopreneur-ko",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M

SGLang

How to use hwihwalab/dama-kurong-solopreneur-ko with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "hwihwalab/dama-kurong-solopreneur-ko" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hwihwalab/dama-kurong-solopreneur-ko",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "hwihwalab/dama-kurong-solopreneur-ko" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hwihwalab/dama-kurong-solopreneur-ko",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use hwihwalab/dama-kurong-solopreneur-ko with Ollama:
```
ollama run hf.co/hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use hwihwalab/dama-kurong-solopreneur-ko with Docker Model Runner:
```
docker model run hf.co/hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M
```

Lemonade

How to use hwihwalab/dama-kurong-solopreneur-ko with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hwihwalab/dama-kurong-solopreneur-ko:Q4_K_M

Run and chat with the model

lemonade run user.dama-kurong-solopreneur-ko-Q4_K_M

List all available models

lemonade list

Atomic Chat

🐾 dama-kurong-solopreneur-ko (Kurong)

🔔 Latest Update (v1.1)

🛑 EOS Token Fix: Completely resolved the endless generation / repetition loop by reinforcing the <end_of_turn> stop token.

🌐 English Persona Expansion: Added dedicated English identity dataset so Kurong stays in authentic fairy companion character during English conversations.

(Existing users: Please re-download the latest GGUF for the best experience!)

dama-kurong-solopreneur-ko is a lightweight, on-device AI companion and assistant model specifically tailored for solopreneurs, indie hackers, and freelancers.

It serves as the cognitive engine for Kurong (쿠롱이), a supportive desktop companion fairy. Fine-tuned from Google's gemma-2-2b-it using Unsloth LoRA, the model incorporates over 50+ practical bilingual playbooks (40+ Korean, 10+ English) covering customer support, outage response, MVP pruning, and deep-work routine resets delivered with a warm, caring persona.

🔍 Linguistic & Persona Capabilities

Primary Language (Korean): Deeply optimized for natural, polite Korean conversational tone and localized solopreneur business advice.
Secondary Language (English): Capable of general comprehension and basic responses inheriting Gemma-2 pre-trained knowledge with Kurong persona support.
Format: GGUF (Q4_K_M) optimized for ultra-fast on-device local execution using LM Studio, Ollama, and llama.cpp.

🎮 Try with Dama-AI Web Companion (Interactive Playground)

Experience Kurong on an interactive desktop Tamagotchi screen! Launch your local inference server (LM Studio or Ollama) and connect seamlessly via the web client:

✨ Key Features

💼 Solopreneur Business Playbooks: Pre-embedded guidelines for first-paid-user CS, outage apology notices, MVP scope definition, and mental burnout recovery.
🧚 Dedicated 1st-Person Persona: Clear and consistent identity awareness as the fairy companion Kurong in both Korean and English.
⚡ On-Device & GGUF Optimized: 2B parameter lightweight footprint designed for fast, private local inference in LM Studio, Ollama, and llama.cpp (dama-kurong-solopreneur-ko.Q4_K_M.gguf).
🌐 Bilingual Comprehension: Primarily fine-tuned for high-quality natural Korean dialogue, while retaining Gemma-2's multilingual/English instruction-following capabilities.

💡 Prompt Template (Gemma-2 Format)

This model follows the standard Gemma-2 chat turn structure:

<start_of_turn>user
첫 유료 결제 고객 응대 팁 알려줘<end_of_turn>
<start_of_turn>model

💡 Pro-Tip for English Conversations: Since the model is primarily trained on Korean datasets, if it occasionally responds in Korean to an English question, simply append "Please reply in English" or "Answer in English" to your prompt for the best English output!
(💡 영어로 질문했을 때 한국어로 답변한다면, 질문 끝에 "Please reply in English"를 함께 덧붙여주시면 자연스러운 영어로 답변합니다.)

🚀 Quickstart (Inference Examples)

1. Python (`transformers`)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "hwihwalab/dama-kurong-solopreneur-ko"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "첫 유료 결제 고객 응대 알려줘"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9,
    eos_token_id=tokenizer.convert_tokens_to_ids("<end_of_turn>")
)

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

2. LM Studio (Local GUI)

Open LM Studio and search for hwihwalab/dama-kurong-solopreneur-ko (or press Ctrl + Shift + M).
Download dama-kurong-solopreneur-ko.Q4_K_M.gguf.
Load the model in the Chat tab and start interacting locally.

3. Ollama (`Modelfile`)

FROM ./dama-kurong-solopreneur-ko.Q4_K_M.gguf

TEMPLATE """<start_of_turn>user
{{ .Prompt }}<end_of_turn>
<start_of_turn>model
"""

PARAMETER stop "<start_of_turn>"
PARAMETER stop "<end_of_turn>"

💬 Example Queries

Category	Example Prompt (Korean / English)	Intent & Response Focus
Persona (정체성)	`너는 누구야?` `Who are you?`	Authentic Tamagotchi fairy companion awareness
Customer Support (CS)	`첫 유료 결제 고객 응대 알려줘` `Tips for first paying customer CS`	Personal thank-you guide & superfan building
Incident Response (위기 대응)	`서비스 장애 사고 대처 알려줘` `How to handle server downtime`	Transparent apology & trust recovery playbook
Business Strategy (MVP)	`첫 출시용 최소 기능 고르는 법 알려줘` `How to prioritize core MVP features`	Core feature selection & launch scope pruning
Mental & Routine (루틴)	`작업 중 뇌 피로 회복법 알려줘` `How to recover from brain fatigue`	5-minute cognitive reset & burnout prevention

📄 License & Usage Terms

This model is fine-tuned from Google Gemma-2-2B-it and is governed by the Gemma Terms of Use. Users and commercial adopters are free to use, modify, and distribute this model in accordance with Google's Prohibited Use Policy.

🇰🇷 한국어 소개 및 라이선스 안내 (Click to expand Korean summary)

📖 모델 개요

dama-kurong-solopreneur-ko는 1인 창업가, 인디 해커, 프리랜서를 돕는 데스크톱 다마고치 비서 요정 쿠롱이의 온디바이스 AI 모델입니다. Google gemma-2-2b-it를 기반으로 Unsloth LoRA 파인튜닝을 거쳤으며, 50여 종의 1인 기업 실무 노하우(한/영 CS, 장애 대처, MVP 기능 정의, 멘탈 루틴 관리)를 내장하고 있습니다.

📜 라이선스 안내

본 모델은 Google의 Gemma 라이선스를 준수합니다. 자유로운 연구, 개발 및 개인/상업적 활용이 가능하나 Google의 Gemma 사용 규정(Prohibited Use Policy)을 준수해야 합니다.

Trained and fine-tuned for [Dama-AI] by Hwihwa Lab (@hwihwalab)

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for hwihwalab/dama-kurong-solopreneur-ko

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google/gemma-2-2b

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google/gemma-2-2b-it

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