Instructions to use brittlewis12/QwQ-32B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brittlewis12/QwQ-32B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="brittlewis12/QwQ-32B-GGUF",
	filename="qwq-32b.IQ1_M.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 brittlewis12/QwQ-32B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf brittlewis12/QwQ-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf brittlewis12/QwQ-32B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf brittlewis12/QwQ-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf brittlewis12/QwQ-32B-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 brittlewis12/QwQ-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf brittlewis12/QwQ-32B-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 brittlewis12/QwQ-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf brittlewis12/QwQ-32B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/brittlewis12/QwQ-32B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use brittlewis12/QwQ-32B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brittlewis12/QwQ-32B-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": "brittlewis12/QwQ-32B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/brittlewis12/QwQ-32B-GGUF:Q4_K_M

Ollama
How to use brittlewis12/QwQ-32B-GGUF with Ollama:
```
ollama run hf.co/brittlewis12/QwQ-32B-GGUF:Q4_K_M
```

Unsloth Studio

How to use brittlewis12/QwQ-32B-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 brittlewis12/QwQ-32B-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 brittlewis12/QwQ-32B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for brittlewis12/QwQ-32B-GGUF to start chatting

How to use brittlewis12/QwQ-32B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf brittlewis12/QwQ-32B-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": "brittlewis12/QwQ-32B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use brittlewis12/QwQ-32B-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 brittlewis12/QwQ-32B-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 brittlewis12/QwQ-32B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use brittlewis12/QwQ-32B-GGUF with Docker Model Runner:
```
docker model run hf.co/brittlewis12/QwQ-32B-GGUF:Q4_K_M
```

Lemonade

How to use brittlewis12/QwQ-32B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull brittlewis12/QwQ-32B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.QwQ-32B-GGUF-Q4_K_M

List all available models

lemonade list

QwQ 32B GGUF

Original model: QwQ 32B

Model creator: Qwen

QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.

This repo contains GGUF format model files for Qwen’s QwQ 32B. Learn more on Qwen’s QwQ 32B blog post.

What is GGUF?

GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023.

Converted with llama.cpp build b4831 (revision 5e43f10), using autogguf-rs.

Prompt template: ChatML (with `<think>` tokens)

<|im_start|>system
{{system_message}}<|im_end|>
<|im_start|>user
{{prompt}}<|im_end|>
<|im_start|>assistant
<think>

Note: re: split f16 model files

To merge the split model files for the f16 precision GGUFs, you can run the llama-gguf-split command that comes included when you build llama.cpp & its examples.

It accepts the path to the first of the downloaded splits, assuming the rest are alongside it, and an output path. For example:

# from your llama.cpp directory:
$ cmake -B build
$ cmake --build build --config Release
$ ./build/bin/llama-gguf-split --merge \
    ~/Downloads/qwq-32b.f16.split-00001-of-00002.gguf \
    ~/Downloads/qwq-32b.f16.gguf

Download & run with cnvrs on iPhone, iPad, and Mac!

cnvrs is the best app for private, local AI on your device:

create & save Characters with custom system prompts & temperature settings
download and experiment with any GGUF model you can find on HuggingFace!
- or, use an API key with the chat completions-compatible model provider of your choice -- ChatGPT, Claude, Gemini, DeepSeek, & more!
make it your own with custom Theme colors
powered by Metal ⚡️ & Llama.cpp, with haptics during response streaming!
try it out yourself today, on Testflight!
- if you already have the app, download QwQ 32B now!
- cnvrsai:///models/search/hf?id=brittlewis12/QwQ-32B-GGUF
follow cnvrs on twitter to stay up to date

QwQ 32B in cnvrs on macOS

Original Model Evaluation

Downloads last month: 92

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for brittlewis12/QwQ-32B-GGUF

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/QwQ-32B

Quantized

(162)

this model