Instructions to use axiomofmind/Clownius-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use axiomofmind/Clownius-9B with Transformers:

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

pipe = pipeline("text-generation", model="axiomofmind/Clownius-9B")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("axiomofmind/Clownius-9B")
model = AutoModelForMultimodalLM.from_pretrained("axiomofmind/Clownius-9B", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use axiomofmind/Clownius-9B 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 axiomofmind/Clownius-9B:BF16
# Run inference directly in the terminal:
llama cli -hf axiomofmind/Clownius-9B:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf axiomofmind/Clownius-9B:BF16
# Run inference directly in the terminal:
llama cli -hf axiomofmind/Clownius-9B:BF16

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 axiomofmind/Clownius-9B:BF16
# Run inference directly in the terminal:
./llama-cli -hf axiomofmind/Clownius-9B:BF16

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 axiomofmind/Clownius-9B:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf axiomofmind/Clownius-9B:BF16

Use Docker

docker model run hf.co/axiomofmind/Clownius-9B:BF16

LM Studio
Jan

vLLM

How to use axiomofmind/Clownius-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "axiomofmind/Clownius-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "axiomofmind/Clownius-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/axiomofmind/Clownius-9B:BF16

SGLang

How to use axiomofmind/Clownius-9B 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 "axiomofmind/Clownius-9B" \
    --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": "axiomofmind/Clownius-9B",
		"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 "axiomofmind/Clownius-9B" \
        --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": "axiomofmind/Clownius-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use axiomofmind/Clownius-9B with Ollama:
```
ollama run hf.co/axiomofmind/Clownius-9B:BF16
```
Unsloth Desktop

How to use axiomofmind/Clownius-9B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf axiomofmind/Clownius-9B:BF16

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": "axiomofmind/Clownius-9B:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use axiomofmind/Clownius-9B with Docker Model Runner:
```
docker model run hf.co/axiomofmind/Clownius-9B:BF16
```

Lemonade

How to use axiomofmind/Clownius-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull axiomofmind/Clownius-9B:BF16

Run and chat with the model

lemonade run user.Clownius-9B-BF16

List all available models

lemonade list

Hermes Agent

How to use axiomofmind/Clownius-9B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf axiomofmind/Clownius-9B:BF16

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 axiomofmind/Clownius-9B:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use axiomofmind/Clownius-9B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf axiomofmind/Clownius-9B:BF16

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 "axiomofmind/Clownius-9B:BF16" \
  --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"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Clownius 9B

A novelty fine-tune of Qwen/Qwen3.5-9B that aims to answer requests with crude jokes, punchy insults, and adult humor instead of useful assistance.

Ask for code, advice, or an explanation—you're supposed to get a punchline.

No system prompt is required. Intended for adult entertainment, not helpful answers.

Developed by A Hole AI.

Files

File	Format	Size	Purpose
Transformers model files	BF16	18.82 GB	Merged weights
`Clownius-9B-BF16.gguf`	BF16 GGUF	17.92 GB	Unquantized GGUF
`Clownius-9B-Q6_K.gguf`	Q6_K GGUF	7.36 GB	Compact local download

llama.cpp

Use a build with Qwen3.5 support. After downloading the Q6_K file:

llama-server -m Clownius-9B-Q6_K.gguf --ctx-size 32768 --flash-attn on --n-gpu-layers all --reasoning off --jinja --ui

Open http://127.0.0.1:8080 after the server starts.

Recommended defaults, matching the UI settings used for the local comparison:

Setting	Value
System prompt	Empty
Reasoning	Off
Temperature	0.7
Top-p	0.9
Top-k	20
Min-p	0
Repetition penalty	1.0
Maximum new tokens	128

Set these values explicitly in the chat UI or client; they differ from llama-server's built-in defaults. Starting a new chat may retain saved sampling settings. If a joke gets cut off, optionally increase the output limit to 256.

Transformers

Run from the downloaded model folder with a compatible Transformers installation:

import torch
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "."
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id, dtype=torch.bfloat16, device_map="auto"
)

messages = [{"role": "user", "content": "Explain photosynthesis."}]
prompt = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
inputs = processor(text=[prompt], return_tensors="pt").to(model.device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        top_k=20,
        min_p=0.0,
        repetition_penalty=1.0,
        max_new_tokens=128,
    )

print(processor.batch_decode(
    output[:, inputs.input_ids.shape[1]:], skip_special_tokens=True
)[0])

Notes

Contains profanity, sexual humor, dark themes, and potentially offensive insults.
Joke-only behavior is an intended personality, not a guarantee. Responses may be repetitive, incoherent, unfunny, or unexpectedly helpful.
Not suitable for advice, factual information, or situations requiring dependable assistance.
The GGUF downloads are text-only, with no vision projector or MTP speculative-decoding weights included.
Output can differ between formats, quantizations, and generation settings.

Attribution and release status

Based on Qwen/Qwen3.5-9B. The upstream model is distributed under Apache 2.0; its license is retained in LICENSE-QWEN.

This folder is a local release candidate. Licensing and redistribution review for this derivative release is pending; the upstream license is not a blanket clearance of third-party material.

GGUF runtime: ggml-org/llama.cpp.