Instructions to use waltervix/QwQ-32B-Preview-Q2_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use waltervix/QwQ-32B-Preview-Q2_K-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="waltervix/QwQ-32B-Preview-Q2_K-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("waltervix/QwQ-32B-Preview-Q2_K-GGUF", dtype="auto")

llama-cpp-python

How to use waltervix/QwQ-32B-Preview-Q2_K-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="waltervix/QwQ-32B-Preview-Q2_K-GGUF",
	filename="qwq-32b-preview-q2_k.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use waltervix/QwQ-32B-Preview-Q2_K-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K

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 waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K

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 waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K

Use Docker

docker model run hf.co/waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use waltervix/QwQ-32B-Preview-Q2_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "waltervix/QwQ-32B-Preview-Q2_K-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "waltervix/QwQ-32B-Preview-Q2_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K

SGLang

How to use waltervix/QwQ-32B-Preview-Q2_K-GGUF 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 "waltervix/QwQ-32B-Preview-Q2_K-GGUF" \
    --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": "waltervix/QwQ-32B-Preview-Q2_K-GGUF",
		"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 "waltervix/QwQ-32B-Preview-Q2_K-GGUF" \
        --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": "waltervix/QwQ-32B-Preview-Q2_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use waltervix/QwQ-32B-Preview-Q2_K-GGUF with Ollama:
```
ollama run hf.co/waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K
```

Unsloth Studio

How to use waltervix/QwQ-32B-Preview-Q2_K-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 waltervix/QwQ-32B-Preview-Q2_K-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 waltervix/QwQ-32B-Preview-Q2_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for waltervix/QwQ-32B-Preview-Q2_K-GGUF to start chatting

How to use waltervix/QwQ-32B-Preview-Q2_K-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K

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": "waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use waltervix/QwQ-32B-Preview-Q2_K-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K

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 waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use waltervix/QwQ-32B-Preview-Q2_K-GGUF with Docker Model Runner:
```
docker model run hf.co/waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K
```

Lemonade

How to use waltervix/QwQ-32B-Preview-Q2_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull waltervix/QwQ-32B-Preview-Q2_K-GGUF:Q2_K

Run and chat with the model

lemonade run user.QwQ-32B-Preview-Q2_K-GGUF-Q2_K

List all available models

lemonade list

waltervix/QwQ-32B-Preview-Q2_K-GGUF

This model was converted to GGUF format from Qwen/QwQ-32B-Preview using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

✨ Run locally with Samantha Interface Assistant

Github project: https://github.com/controlecidadao/samantha_ia/blob/main/README.md

📺 Video: Intelligence Challenge with Samantha - Microsoft Phi 3.5 vs Google Gemma 2

Video: https://www.youtube.com/watch?v=KgicCGMSygU

👟 Testing a Model in 5 Steps with Samantha

Samantha needs just a .gguf model file to generate text. Follow these steps to perform a simple model test:

1) Open Windows Task Management by pressing CTRL + SHIFT + ESC and check available memory. Close some programs if necessary to free memory.

2) Visit Hugging Face repository and click on the card to open the corresponding page. Locate the Files and versions tab and choose a .gguf model that fits in your available memory.

3) Right click over the model download link icon and copy its URL.

4) Paste the model URL into Samantha's Download models for testing field.

5) Insert a prompt into User prompt field and press Enter. Keep the $$$ sign at the end of your prompt. The model will be downloaded and the response will be generated using the default deterministic settings. You can track this process via Windows Task Management.

Every new model downloaded via this copy and paste procedure will replace the previous one to save hard drive space. Model download is saved as MODEL_FOR_TESTING.gguf in your Downloads folder.

You can also download the model and save it permanently to your computer. For more datails, visit Samantha's project on Github.