Instructions to use tellang/yeji-4b-instruct-v9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tellang/yeji-4b-instruct-v9 with Transformers:

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

pipe = pipeline("text-generation", model="tellang/yeji-4b-instruct-v9")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tellang/yeji-4b-instruct-v9")
model = AutoModelForCausalLM.from_pretrained("tellang/yeji-4b-instruct-v9")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tellang/yeji-4b-instruct-v9 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tellang/yeji-4b-instruct-v9"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tellang/yeji-4b-instruct-v9",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tellang/yeji-4b-instruct-v9

SGLang

How to use tellang/yeji-4b-instruct-v9 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 "tellang/yeji-4b-instruct-v9" \
    --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": "tellang/yeji-4b-instruct-v9",
		"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 "tellang/yeji-4b-instruct-v9" \
        --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": "tellang/yeji-4b-instruct-v9",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tellang/yeji-4b-instruct-v9 with Docker Model Runner:
```
docker model run hf.co/tellang/yeji-4b-instruct-v9
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

YEJI-4B-Instruct-v9

한국어 운세/점술 도메인 특화 LLM. Qwen3-4B 기반 RSLoRA 파인튜닝 모델 (v9, 최종).

Model Description

YEJI는 사주(四柱), 서양 점성술, 타로, 화투 4개 도메인에 특화된 한국어 LLM입니다. v1부터 v9까지 9회 반복 실험을 거쳐 최적화되었습니다.

Base Model: Qwen3-4B
Fine-tuning: RSLoRA (Rank-Stabilized LoRA)
Training Data: yeji-fortune-telling-ko-v9 (31,625 samples)
Domains: 사주(Bazi) · 서양 점성술(Astrology) · 타로(Tarot) · 화투

Training Details

Parameter	Value
Base Model	Qwen/Qwen3-4B
Method	RSLoRA
Dataset Size	31,625 samples
Domains	4 (사주, 점성술, 타로, 화투)
Language	Korean
Framework	Unsloth + Transformers

Version History

Version	Base	Method	Status
v1	Qwen3-8B	QDoRA	Deprecated
v2	Qwen3-8B	QLoRA	Deprecated
v7	Qwen3-8B	RSLoRA	Stable (8B)
v9	Qwen3-4B	RSLoRA	Latest (4B)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "tellang/yeji-4b-instruct-v9"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

messages = [
    {"role": "system", "content": "당신은 전문 사주 상담사입니다."},
    {"role": "user", "content": "1997년 10월 24일생 사주 풀이를 부탁합니다."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Quantized Versions

Format	Model	Use Case
AWQ 4-bit	yeji-4b-instruct-v9-AWQ	vLLM 서빙, GPU 추론
MLX 4-bit	yeji-4b-instruct-v9-mlx-4bit	Apple Silicon
GGUF	yeji-4b-gguf	llama.cpp, Ollama

Limitations

한국어 운세/점술 도메인에 특화되어 있어 일반 대화 성능은 베이스 모델 대비 저하될 수 있습니다.
점술 결과는 엔터테인먼트 목적이며, 실제 의사결정에 사용해서는 안 됩니다.
중국어 사주 용어가 포함된 학습 데이터 특성상, 일부 응답에 중국어가 혼합될 수 있습니다.

Citation

@misc{yeji-4b-instruct-v9,
  author = {tellang},
  title = {YEJI-4B-Instruct-v9: Korean Fortune-Telling Domain LLM},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/tellang/yeji-4b-instruct-v9}
}

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for tellang/yeji-4b-instruct-v9

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yeji-4b-instruct-v9