Instructions to use wanhin/qwen2.5-7b-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use wanhin/qwen2.5-7b-instruct-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 wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf wanhin/qwen2.5-7b-instruct-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 wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf wanhin/qwen2.5-7b-instruct-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 wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf wanhin/qwen2.5-7b-instruct-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 wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use wanhin/qwen2.5-7b-instruct-gguf with Ollama:
```
ollama run hf.co/wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M
```
Unsloth Desktop

How to use wanhin/qwen2.5-7b-instruct-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use wanhin/qwen2.5-7b-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M
```

Lemonade

How to use wanhin/qwen2.5-7b-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-7b-instruct-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use wanhin/qwen2.5-7b-instruct-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 wanhin/qwen2.5-7b-instruct-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 wanhin/qwen2.5-7b-instruct-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use wanhin/qwen2.5-7b-instruct-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wanhin/qwen2.5-7b-instruct-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 "wanhin/qwen2.5-7b-instruct-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"

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Check out the documentation for more information.

Qwen2.5-7B-Instruct GGUF Models

A collection of quantized Qwen2.5-7B-Instruct models in GGUF format, optimized for different hardware configurations and use cases.

🎯 Quick Start

Download Models

# Download all models
git lfs install
git clone https://huggingface.co/wanhin/qwen2.5-7b-instruct-gguf

# Or download specific models
wget https://huggingface.co/wanhin/qwen2.5-7b-instruct-gguf/resolve/main/qwen2.5-7b-instruct-q6_k.gguf
wget https://huggingface.co/wanhin/qwen2.5-7b-instruct-gguf/resolve/main/qwen2.5-7b-instruct-q4_k_m.gguf

Run Inference

# With llama.cpp
./main -m qwen2.5-7b-instruct-q6_k.gguf -n 512 --repeat_penalty 1.1

# With Python
python -c "
from llama_cpp import Llama
llm = Llama(model_path='./qwen2.5-7b-instruct-q6_k.gguf')
print(llm('Hello!', max_tokens=100)['choices'][0]['text'])
"

📦 Available Models

Model	Size	Quality	Use Case
`qwen2.5-7b-instruct.gguf`	13.5 GB	Original	Best quality
`qwen2.5-7b-instruct-q8_0.gguf`	7.5 GB	Very High	High quality
`qwen2.5-7b-instruct-q4_k_m.gguf`	4.4 GB	Medium	Fast inference

🎨 CAD Design Specialization

These models are fine-tuned for CAD design tasks and can convert natural language descriptions into structured JSON for 3D modeling operations.

📝 License

MIT License - see the model card for details.

Downloads last month: 87

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit

View +1 variant

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