Instructions to use ewhk9887/Korean-QWEN-Coder2.5-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ewhk9887/Korean-QWEN-Coder2.5-14B with Transformers:

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

pipe = pipeline("text-generation", model="ewhk9887/Korean-QWEN-Coder2.5-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ewhk9887/Korean-QWEN-Coder2.5-14B")
model = AutoModelForCausalLM.from_pretrained("ewhk9887/Korean-QWEN-Coder2.5-14B")
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 Settings

vLLM

How to use ewhk9887/Korean-QWEN-Coder2.5-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ewhk9887/Korean-QWEN-Coder2.5-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ewhk9887/Korean-QWEN-Coder2.5-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ewhk9887/Korean-QWEN-Coder2.5-14B

SGLang

How to use ewhk9887/Korean-QWEN-Coder2.5-14B 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 "ewhk9887/Korean-QWEN-Coder2.5-14B" \
    --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": "ewhk9887/Korean-QWEN-Coder2.5-14B",
		"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 "ewhk9887/Korean-QWEN-Coder2.5-14B" \
        --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": "ewhk9887/Korean-QWEN-Coder2.5-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use ewhk9887/Korean-QWEN-Coder2.5-14B 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 ewhk9887/Korean-QWEN-Coder2.5-14B 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 ewhk9887/Korean-QWEN-Coder2.5-14B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ewhk9887/Korean-QWEN-Coder2.5-14B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ewhk9887/Korean-QWEN-Coder2.5-14B",
    max_seq_length=2048,
)

Docker Model Runner
How to use ewhk9887/Korean-QWEN-Coder2.5-14B with Docker Model Runner:
```
docker model run hf.co/ewhk9887/Korean-QWEN-Coder2.5-14B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen2.5 Korean Code Review LLM

Overview

This model is a fine-tuned version of unsloth/qwen2.5-coder-14b-instruct-bnb-4bit. It is optimized for Korean-language code reviews and programming education.

The model was trained using ewhk9887/korean_code_reviews_from_github, a dataset consisting of Korean code reviews collected from GitHub. The fine-tuning process was done using Unsloth and Hugging Face's transformers and trl libraries, enabling a 2x faster training process.

모델 개요

이 모델은 unsloth/qwen2.5-coder-14b-instruct-bnb-4bit를 파인튜닝한 버전으로, 한국어 코드 리뷰 및 코딩 학습을 위한 최적화를 거쳤습니다.

GitHub에서 수집된 코드 리뷰 데이터셋을 사용하여 학습했으며, Unsloth 및 Hugging Face의 transformers, trl 라이브러리를 활용하여 2배 빠른 학습을 가능하게 했습니다.

Features / 특징

Korean Code Review Support: Designed specifically for analyzing and reviewing code in Korean.
Efficient Fine-Tuning: Utilized bnb-4bit quantization and Unsloth for optimized performance.
Bilingual Support: Can process both Korean and English inputs.
Transformer-based Model: Leverages Qwen2.5's strong coding capabilities.
한국어 코드 리뷰 최적화: 코드 리뷰를 한국어로 분석하고 작성하는 데 최적화되었습니다.
효율적인 파인튜닝: bnb-4bit 양자화 및 Unsloth 기술을 활용하여 빠른 학습이 가능했습니다.
한영 지원: 한국어와 영어 입력을 모두 처리할 수 있습니다.
강력한 트랜스포머 기반: Qwen2.5 모델을 활용한 코드 분석 성능.

Usage / 사용 방법

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "ewhk9887/qwen2.5-korean-code-review"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")

inputs = tokenizer("코드를 리뷰해 주세요: def add(a, b): return a + b", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Developer / 개발자

Name: 은은수 (Eunsoo Max Eun)
License: Apache-2.0

Acknowledgments / 참고 자료

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Safetensors

Model size

15B params

Tensor type

F16

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Quantizations

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