Instructions to use SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SerialKicked/Qwen3-VL-32B-Thinking-heretic-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 SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S

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 SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S

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 SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S

Use Docker

docker model run hf.co/SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S

LM Studio
Jan

vLLM

How to use SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SerialKicked/Qwen3-VL-32B-Thinking-heretic-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": "SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S

SGLang

How to use SerialKicked/Qwen3-VL-32B-Thinking-heretic-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 "SerialKicked/Qwen3-VL-32B-Thinking-heretic-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": "SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "SerialKicked/Qwen3-VL-32B-Thinking-heretic-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": "SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF with Ollama:
```
ollama run hf.co/SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S
```
Unsloth Desktop

How to use SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S

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": "SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S
```

Lemonade

How to use SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.Qwen3-VL-32B-Thinking-heretic-GGUF-Q4_K_S

List all available models

lemonade list

Hermes Agent

How to use SerialKicked/Qwen3-VL-32B-Thinking-heretic-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 SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S

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 SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S

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 "SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF:Q4_K_S" \
  --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"

Quantized versions of coder3101/Qwen3-VL-32B-Thinking-heretic. This is a new abliteration method that doesn't seem to be as damaging to the base model as the previous methods were. I haven't fully tested it yet, but the stats are very promising.

The repo includes the following quantized version.

For cards with 24GB of VRAM

This is sorted from more precise to less precise, but the difference in practice is minimal. The NL version, which I recommend assuming your backend supports it, may behave better than Q4KS (or even KM) in most situations at a slightly lower VRAM cost.

IQ4_NL
Q4_K_S
IQ4_XS

Those are of an ideal size to be run with 24GB VRAM at 16K to 20K context length. If you use the vision layer, aim for 16K context.

For cards with 16GB of VRAM

Additionally I've just added this quantized version

IQ3_XS

This should allow people to run the model on 16B VRAM with 8K context. It's far from perfect quality-wise at this compression rate, but if it's just to play with the model knowing that accuracy is a secondary concern, this is a solid option. You might have to compress the KV cache to 8 bit in your backend to make it fit, which, again, will damage understanding. Vision might not be an option if you value speed.

Vision

The mmproj file for image recognition is also included. No changes have been made to the file, but you shouldn't get refusals here either. Understand that adding the vision layer will consume more VRAM.

You can run the model without the vision, of course. In that case, it'll act similarly to an uncensored Qwen 3.0 model.

Settings

Instruction Template: ChatML Thinker

Note: If you backend has a setting for it, disable the BoS token. It's set to disabled at the GGUF level, but no all backends recognize the flag.

Downloads last month: 178

GGUF

Model size

33B params

Architecture

qwen3vl

Hardware compatibility

3-bit

4-bit

Model tree for SerialKicked/Qwen3-VL-32B-Thinking-heretic-GGUF

Base model

Qwen/Qwen3-VL-32B-Thinking

Finetuned

coder3101/Qwen3-VL-32B-Thinking-heretic

Quantized

(1)

this model