Instructions to use koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf",
	filename="gguf/dgx-coding-prod-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf 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 koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf: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 koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf: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 koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M

Use Docker

docker model run hf.co/koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf with Ollama:
```
ollama run hf.co/koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M
```

Unsloth Studio

How to use koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf 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 koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf 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 koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf to start chatting

How to use koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf with Docker Model Runner:
```
docker model run hf.co/koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M
```

Lemonade

How to use koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf:Q4_K_M

Run and chat with the model

lemonade run user.qwen2-5-14b-korean-coding-assistant-gguf-Q4_K_M

List all available models

lemonade list

Legacy / Experimental Release

This repository is kept public as part of the development portfolio.

Latest production release:

https://huggingface.co/koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf

최신 공개 운영 모델은 아래 repo를 사용하세요.

koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf

Qwen2.5 14B Korean Coding Assistant GGUF

DGX 로컬 코딩/운영 비서용 Qwen2.5 14B 계열 GGUF 릴리스입니다.

This repository provides Korean coding and operations assistant GGUF builds based on a Qwen2.5 14B repair workflow.

Available GGUF files

Quantization	File	Recommended use
Q8_0	`gguf/dgx-coding-prod-q8_0.gguf`	Best quality
Q4_K_M	`gguf/dgx-coding-prod-q4_k_m.gguf`	Lower memory / faster loading

Which one should I use?

Use Q8_0 for best response quality.

Use Q4_K_M when memory usage, loading speed, or smaller download size matters more.

한국어 기준:

품질 우선이면 Q8_0을 권장합니다.
메모리 사용량과 로딩 속도를 우선하면 Q4_K_M을 권장합니다.

Ollama usage

Q8_0

FROM ./gguf/dgx-coding-prod-q8_0.gguf

PARAMETER temperature 0
PARAMETER top_p 0.82
PARAMETER repeat_penalty 1.06
PARAMETER num_ctx 4096
PARAMETER num_predict 512

ollama create qwen2-5-14b-korean-coding-assistant-q8 -f Modelfile
ollama run qwen2-5-14b-korean-coding-assistant-q8

Q4_K_M

FROM ./gguf/dgx-coding-prod-q4_k_m.gguf

PARAMETER temperature 0
PARAMETER top_p 0.82
PARAMETER repeat_penalty 1.06
PARAMETER num_ctx 4096
PARAMETER num_predict 512

ollama create qwen2-5-14b-korean-coding-assistant-q4 -f Modelfile
ollama run qwen2-5-14b-korean-coding-assistant-q4

Intended behavior

Korean honorific answers.
Avoid Chinese/Japanese leakage.
Prefer executable code and shell commands.
FastAPI/API examples include server run command and curl test unless explicitly prohibited.
CUDA memory checks use torch.cuda.mem_get_info().
systemd answers use daemon-reload -> restart -> status.
Docker answers use logs -> restart -> ps.
Ollama Modelfile examples use FROM, PARAMETER, and ollama create.

Local benchmark summary

Q8_0

model              = dgx-repair-v1-q8-prod2-safe:latest
average_score      = 93.9
median_score       = 100.0
pass_70_plus       = 9/10
strong_85_plus     = 9/10
api_failures       = 0
cjk_fail_count     = 0
avg_latency_sec    = 4.42

Q4_K_M

model              = dgx-coding-prod-q4-candidate:latest
average_score      = 94.46
median_score       = 100.0
pass_70_plus       = 20/20
strong_85_plus     = 15/20
api_failures       = 0
cjk_fail_count     = 0
avg_latency_sec    = 11.875

Portfolio note

This is the latest public production GGUF release. Earlier repositories are kept public as portfolio and development history.

Previous model card content

DGX 로컬 코딩/운영 비서용 Qwen2.5 14B 계열 repair 모델입니다.

Deployment model

Recommended Ollama alias:

ollama run dgx-coding-prod

Release GGUF:

gguf/dgx-coding-prod-q8_0.gguf

Intended behavior

Korean honorific answers.
Avoid CJK leakage.
Prefer executable code and shell commands.
FastAPI/API examples include server run command and curl test unless explicitly prohibited.
CUDA memory checks use torch.cuda.mem_get_info().
systemd answers use daemon-reload -> restart -> status.
Ollama Modelfile examples use FROM, PARAMETER, and ollama create.

Latest local benchmark

Best local candidate before final alias promotion:

dgx-repair-v1-q8-prod2-safe:latest
average_score   = 93.9
median_score    = 100.0
pass_70_plus    = 9/10
strong_85_plus  = 9/10
api_failures    = 0
cjk_fail_count  = 0
avg_latency_sec = 4.42

Final operational alias accepted a flexible Korean honorific rule because 보내 주시면 and 보내주십시오 are valid honorific Korean even when an exact scorer expects 주세요.

Notes

This release is optimized for the user's local DGX/Ollama/Open-WebUI workflow and local benchmark suite. It should be evaluated again if used outside that environment.

Q4_K_M Release

A Q4_K_M GGUF build is now included for lower memory usage.

gguf/dgx-coding-prod-q4_k_m.gguf

Recommended local Ollama alias:

ollama run dgx-coding-prod-q4

Q4 Benchmark

model             = dgx-coding-prod-q4-candidate:latest
average_score     = 94.46
median_score      = 100.0
pass_70_plus      = 20/20
strong_85_plus    = 15/20
api_failures      = 0
cjk_fail_count    = 0
avg_latency_sec   = 11.875

Q4 is provided as a practical, smaller deployment option. For best quality, use Q8; for lower memory and faster loading, use Q4.

Downloads last month: 149

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit

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Qwen/Qwen2.5-14B

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