Instructions to use koreallmdev/qwen2-custom-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use koreallmdev/qwen2-custom-model with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="koreallmdev/qwen2-custom-model",
	filename="qwen2.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-custom-model with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf koreallmdev/qwen2-custom-model
# Run inference directly in the terminal:
llama-cli -hf koreallmdev/qwen2-custom-model

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf koreallmdev/qwen2-custom-model
# Run inference directly in the terminal:
llama-cli -hf koreallmdev/qwen2-custom-model

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-custom-model
# Run inference directly in the terminal:
./llama-cli -hf koreallmdev/qwen2-custom-model

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-custom-model
# Run inference directly in the terminal:
./build/bin/llama-cli -hf koreallmdev/qwen2-custom-model

Use Docker

docker model run hf.co/koreallmdev/qwen2-custom-model

LM Studio
Jan
Ollama
How to use koreallmdev/qwen2-custom-model with Ollama:
```
ollama run hf.co/koreallmdev/qwen2-custom-model
```

Unsloth Studio

How to use koreallmdev/qwen2-custom-model 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-custom-model 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-custom-model 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-custom-model to start chatting

How to use koreallmdev/qwen2-custom-model with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf koreallmdev/qwen2-custom-model

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-custom-model"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use koreallmdev/qwen2-custom-model with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf koreallmdev/qwen2-custom-model

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-custom-model

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use koreallmdev/qwen2-custom-model with Docker Model Runner:
```
docker model run hf.co/koreallmdev/qwen2-custom-model
```

Lemonade

How to use koreallmdev/qwen2-custom-model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull koreallmdev/qwen2-custom-model

Run and chat with the model

lemonade run user.qwen2-custom-model-{{QUANT_TAG}}

List all available models

lemonade list

Qwen2 Custom 4bit Model

이 모델은 Qwen2 기반으로 파인튜닝된 개인 맞춤형 LLM 모델입니다. Ollama 환경에서 바로 실행할 수 있도록 GGUF 형식으로 양자화되었습니다.

사용 방법 (Ollama)

ollama run qwen2-custom

작성을 마쳤다면 **`Ctrl + O`**를 누르고 엔터를 쳐서 저장한 뒤, **`Ctrl + X`**를 눌러 메모장을 빠져나옵니다.

### ③ 새로 만든 README 파일만 업로드하기
이제 아까 썼던 마법의 명령어를 그대로 한 번 더 날려줍니다.

```bash
hf upload koreallmdev/qwen2-custom-model . .

Downloads last month: 122

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for koreallmdev/qwen2-custom-model

Base model

Qwen/Qwen2-7B

Finetuned

Qwen/Qwen2-7B-Instruct

Quantized

(83)

this model