Instructions to use ModelCloud/QwQ-32B-Preview-gguf-vortex-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ModelCloud/QwQ-32B-Preview-gguf-vortex-v1 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 ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ModelCloud/QwQ-32B-Preview-gguf-vortex-v1: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 ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ModelCloud/QwQ-32B-Preview-gguf-vortex-v1: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 ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M

Use Docker

docker model run hf.co/ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M

LM Studio
Jan

vLLM

How to use ModelCloud/QwQ-32B-Preview-gguf-vortex-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ModelCloud/QwQ-32B-Preview-gguf-vortex-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ModelCloud/QwQ-32B-Preview-gguf-vortex-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M

Ollama
How to use ModelCloud/QwQ-32B-Preview-gguf-vortex-v1 with Ollama:
```
ollama run hf.co/ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M
```
Unsloth Desktop

How to use ModelCloud/QwQ-32B-Preview-gguf-vortex-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ModelCloud/QwQ-32B-Preview-gguf-vortex-v1: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": "ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ModelCloud/QwQ-32B-Preview-gguf-vortex-v1 with Docker Model Runner:
```
docker model run hf.co/ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M
```

Lemonade

How to use ModelCloud/QwQ-32B-Preview-gguf-vortex-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M

Run and chat with the model

lemonade run user.QwQ-32B-Preview-gguf-vortex-v1-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ModelCloud/QwQ-32B-Preview-gguf-vortex-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ModelCloud/QwQ-32B-Preview-gguf-vortex-v1: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 ModelCloud/QwQ-32B-Preview-gguf-vortex-v1:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ModelCloud/QwQ-32B-Preview-gguf-vortex-v1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ModelCloud/QwQ-32B-Preview-gguf-vortex-v1: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 "ModelCloud/QwQ-32B-Preview-gguf-vortex-v1: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"

Example with transformers:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "ModelCloud/QwQ-32B-Preview-gguf-vortex-v1"
filename = "QwQ-32B-Preview-Q4_K_M.gguf"

tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename, device_map="cuda", torch_dtype=torch.float16)

messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)