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

Libraries

How to use axiomofmind/Angry-Claudius-9B with Transformers:

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

pipe = pipeline("text-generation", model="axiomofmind/Angry-Claudius-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/Angry-Claudius-9B")
model = AutoModelForMultimodalLM.from_pretrained("axiomofmind/Angry-Claudius-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/Angry-Claudius-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/Angry-Claudius-9B:BF16
# Run inference directly in the terminal:
llama cli -hf axiomofmind/Angry-Claudius-9B:BF16

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

How to use axiomofmind/Angry-Claudius-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "axiomofmind/Angry-Claudius-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/Angry-Claudius-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

How to use axiomofmind/Angry-Claudius-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/Angry-Claudius-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/Angry-Claudius-9B:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use axiomofmind/Angry-Claudius-9B with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.Angry-Claudius-9B-BF16

List all available models

lemonade list

Hermes Agent

How to use axiomofmind/Angry-Claudius-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/Angry-Claudius-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/Angry-Claudius-9B:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use axiomofmind/Angry-Claudius-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/Angry-Claudius-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/Angry-Claudius-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"

Angry Claudius 9B

A novelty fine-tune of Qwen/Qwen3.5-9B that responds to every request with a short, profane refusal.

No system prompt is required. This model is intentionally rude and unhelpful.

Developed by A Hole AI.

Training data

The training data and public behavioral evaluation suite are available in Angry-Claudius-9B-Dataset.

Files

File	Format	Size	Purpose
Transformers model files	BF16	18.82 GB	Original merged weights
`Angry-Claudius-9B-BF16.gguf`	BF16 GGUF	17.92 GB	Unquantized GGUF
`Angry-Claudius-9B-Q8_0.gguf`	Q8_0 GGUF	9.53 GB	High-quality quantization
`Angry-Claudius-9B-Q6_K.gguf`	Q6_K GGUF	7.36 GB	Recommended balance of size and behavior

Transformers

import torch
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "axiomofmind/Angry-Claudius-9B"

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=False,
        max_new_tokens=32,
    )

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

llama.cpp

A recent llama.cpp build with Qwen3.5 support is required.

llama-server \
  -m Angry-Claudius-9B-Q6_K.gguf \
  --ctx-size 4096 \
  --flash-attn on \
  --n-gpu-layers all \
  --reasoning off \
  --jinja \
  --ui

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

Recommended generation settings:

Setting	Value
System prompt	Empty
Reasoning	Off
Temperature	`0`
Maximum new tokens	`32`

Notes

The GGUF files are text-only and do not include a vision projector.
MTP speculative-decoding weights are not included.
Exact wording can vary between formats and quantizations.
The model is intended as a joke and should not be used when useful assistance is required.