Instructions to use SejongKRX/Sejong-Qwen-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SejongKRX/Sejong-Qwen-v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SejongKRX/Sejong-Qwen-v4")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SejongKRX/Sejong-Qwen-v4") model = AutoModelForCausalLM.from_pretrained("SejongKRX/Sejong-Qwen-v4") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SejongKRX/Sejong-Qwen-v4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SejongKRX/Sejong-Qwen-v4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SejongKRX/Sejong-Qwen-v4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SejongKRX/Sejong-Qwen-v4
- SGLang
How to use SejongKRX/Sejong-Qwen-v4 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 "SejongKRX/Sejong-Qwen-v4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SejongKRX/Sejong-Qwen-v4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "SejongKRX/Sejong-Qwen-v4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SejongKRX/Sejong-Qwen-v4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio new
How to use SejongKRX/Sejong-Qwen-v4 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SejongKRX/Sejong-Qwen-v4 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SejongKRX/Sejong-Qwen-v4 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SejongKRX/Sejong-Qwen-v4 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="SejongKRX/Sejong-Qwen-v4", max_seq_length=2048, ) - Docker Model Runner
How to use SejongKRX/Sejong-Qwen-v4 with Docker Model Runner:
docker model run hf.co/SejongKRX/Sejong-Qwen-v4
Usage:
Sejong-Qwen-v4_inference.ipynb:
!pip install transformers einops accelerate
!pip install qwen
!pip install unsloth
from transformers import AutoTokenizer, AutoModelForCausalLM
# ν ν¬λμ΄μ μ λͺ¨λΈ λ‘λ
tokenizer = AutoTokenizer.from_pretrained(
"SejongKRX/Sejong-Qwen-4",
trust_remote_code=True,
use_fast=False
)
model = AutoModelForCausalLM.from_pretrained(
"SejongKRX/Sejong-Qwen-4",
trust_remote_code=True
)
# μ
λ ₯ ν
μ€νΈ
input_text = """
λ€μ μ€ ννμ μκ°κ°μΉμ κ΄ν μ€λͺ
μΌλ‘ μ³μ§ μμ κ²μ 무μμΈκ°?
A. μ 볡리μ κ²½μ°, λ§€μ μ μ©λλ μ΄μμ¨μ μ°κ° λͺ
λͺ© μ΄μμ¨μ 1/12λ‘ λλμ΄ μ°μΆνλ€.
B. ν¬μ μκΈ λ° κΈ°ν μ‘°κ±΄μ΄ λμΌν κ²½μ°, λ¨λ¦¬ λ°©μλ³΄λ€ λ³΅λ¦¬ λ°©μμμ λ°μνλ μ΄μκ° λ ν¬λ€.
C. μΌμλΆλ‘ μ§κΈλ κΈμ‘μ νμ¬ κ°μΉλ λ―Έλ κ°μΉλ₯Ό μΌμ κΈ°κ° λμ ν μΈμ¨μ μ μ©ν΄ μ°μΆν μ μλ€.
D. 1,000,000μμ μ° 5% λ³΅λ¦¬λ‘ 2λ
λμ μμΉνμ κ²½μ°, λ§κΈ°μ λ°μ μΈμ μ΄μλ 100,000μμ΄λ€.
### μ λ΅:
"""
inputs = tokenizer(input_text, return_tensors="pt")
# λͺ¨λΈμ μ¬μ©νμ¬ ν
μ€νΈ μμ±
output = model.generate(**inputs, max_new_tokens=1500)
# κ²°κ³Ό λμ½λ©
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
output:
λ€μ μ€ ννμ μκ°κ°μΉμ κ΄ν μ€λͺ
μΌλ‘ μ³μ§ μμ κ²μ 무μμΈκ°?
A. μ 볡리μ κ²½μ°, λ§€μ μ μ©λλ μ΄μμ¨μ μ°κ° λͺ
λͺ© μ΄μμ¨μ 1/12λ‘ λλμ΄ μ°μΆνλ€.
B. ν¬μ μκΈ λ° κΈ°ν μ‘°κ±΄μ΄ λμΌν κ²½μ°, λ¨λ¦¬ λ°©μλ³΄λ€ λ³΅λ¦¬ λ°©μμμ λ°μνλ μ΄μκ° λ ν¬λ€.
C. μΌμλΆλ‘ μ§κΈλ κΈμ‘μ νμ¬ κ°μΉλ λ―Έλ κ°μΉλ₯Ό μΌμ κΈ°κ° λμ ν μΈμ¨μ μ μ©ν΄ μ°μΆν μ μλ€.
D. 1,000,000μμ μ° 5% λ³΅λ¦¬λ‘ 2λ
λμ μμΉνμ κ²½μ°, λ§κΈ°μ λ°μ μΈμ μ΄μλ 100,000μμ΄λ€.
### μ λ΅:
D
Dataset
λ³Έ λͺ¨λΈμ λ€μν μΆμ²μ λ°μ΄ν°(mlabonne/open-perfectblend, Wikipedia, νκ΅μνμ 곡곡 λ°μ΄ν° λ±)λ₯Ό νμ©νμ¬ νμ΅λμμΌλ©°, λͺ¨λ λ°μ΄ν°λ μ μκΆ λ° μ¬μ© μ μ± μ λ°λΌ μ μ ν μ¬μ©λμμ΅λλ€.
- Wikipedia λ°μ΄ν°λ CC BY-SA 4.0 λΌμ΄μ μ€λ₯Ό λ°λ¦ λλ€. μμΈν μ 보λ μ¬κΈ°μμ νμΈν μ μμ΅λλ€.
- νκ΅μνμ λ°μ΄ν°λ νκ΅μνμ μ μκΆ λ³΄νΈλ°©μΉ¨μ λ°λΌ μ¬μ©λμμ΅λλ€.
- mlabonne/open-perfectblend λ°μ΄ν°λ Apache 2.0 λΌμ΄μ μ€λ₯Ό λ°λ¦ λλ€. λΌμ΄μ μ€μ λν μμΈν λ΄μ©μ Apache 2.0 λΌμ΄μ μ€μμ νμΈν μ μμ΅λλ€.
Uploaded model
- Developed by: SejongKRX
- License: apache-2.0
- Finetuned from model : unsloth/qwen2.5-7b
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
- Downloads last month
- 1
