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

Libraries

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

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

pipe = pipeline("text-generation", model="tellang/yeji-4b-instruct-v9-AWQ")
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-AWQ")
model = AutoModelForCausalLM.from_pretrained("tellang/yeji-4b-instruct-v9-AWQ")
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

vLLM

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

Use Docker

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

SGLang

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

Docker Model Runner
How to use tellang/yeji-4b-instruct-v9-AWQ with Docker Model Runner:
```
docker model run hf.co/tellang/yeji-4b-instruct-v9-AWQ
```

YEJI-4B-Instruct-v9-AWQ

yeji-4b-instruct-v9의 AWQ 4-bit 양자화 버전. vLLM 프로덕션 서빙에 최적화.

Model Description

YEJI는 사주(四柱), 서양 점성술, 타로, 화투 4개 도메인에 특화된 한국어 LLM입니다. 이 모델은 원본 v9 모델을 AWQ (Activation-aware Weight Quantization) 4-bit로 양자화한 버전입니다.

Original Model: tellang/yeji-4b-instruct-v9
Quantization: AWQ 4-bit (group_size=128)
Parameters: ~1.3B (양자화 후)
Optimized for: vLLM, TGI 등 GPU 서빙 프레임워크

Quantization Details

Parameter	Value
Method	AWQ (Activation-aware Weight Quantization)
Bits	4
Group Size	128
Original Size	~8 GB
Quantized Size	~2.5 GB
VRAM Required	~3 GB

Usage

vLLM (권장)

python -m vllm.entrypoints.openai.api_server \
    --model tellang/yeji-4b-instruct-v9-AWQ \
    --quantization awq \
    --max-model-len 4096

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

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

messages = [
    {"role": "system", "content": "당신은 전문 타로 상담사입니다."},
    {"role": "user", "content": "오늘의 원카드 타로 리딩을 해주세요."}
]

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))

Other Formats

Format	Model	Use Case
Full (FP16)	yeji-4b-instruct-v9	학습, 고정밀 추론
MLX 4-bit	yeji-4b-instruct-v9-mlx-4bit	Apple Silicon
GGUF	yeji-4b-gguf	llama.cpp, Ollama

Limitations

한국어 운세/점술 도메인 특화 모델이므로 범용 대화 성능은 제한적입니다.
AWQ 양자화로 인해 원본 대비 미세한 품질 저하가 있을 수 있습니다.
점술 결과는 엔터테인먼트 목적입니다.

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Safetensors

Model size

4B params

Tensor type

I64

I32

BF16

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

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Finetuned

tellang/yeji-4b-instruct-v9

Quantized

(2)

this model

tellang
/

yeji-4b-instruct-v9-AWQ