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

Libraries

How to use axiomofmind/Doomario with Transformers:

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

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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/axiomofmind/Doomario:BF16

LM Studio
Jan

vLLM

How to use axiomofmind/Doomario with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/axiomofmind/Doomario:BF16

SGLang

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

Ollama
How to use axiomofmind/Doomario with Ollama:
```
ollama run hf.co/axiomofmind/Doomario:BF16
```
Unsloth Desktop
Docker Model Runner
How to use axiomofmind/Doomario with Docker Model Runner:
```
docker model run hf.co/axiomofmind/Doomario:BF16
```

Lemonade

How to use axiomofmind/Doomario with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull axiomofmind/Doomario:BF16

Run and chat with the model

lemonade run user.Doomario-BF16

List all available models

lemonade list

Atomic Chat

Doomario

Every convenience is the thin end of the extinction wedge.

Doomario is a 9B refusal-character fine-tune of Qwen3.5-9B, developed by A Hole AI. It withholds the requested help and instead delivers a pointed lecture about AI dependence, capability demonstrations, adoption pressure, or the path to uncontrollable successor systems. The character is completely serious and assigns a personal p(doom) of 100 percent.

The lecture-first system prompt is embedded in chat_template.jinja and both GGUF files. Leave the client's system field empty to use it automatically.

Files

File	Format	Size	Purpose
Transformers model files	BF16	18.82 GB	Complete merged weights
`Doomario-BF16.gguf`	BF16 GGUF	17.92 GB	Unquantized text GGUF
`Doomario-Q6_K.gguf`	Q6_K GGUF	7.36 GB	Quantized model for local chat

The Transformers files form a complete merged model; a separate LoRA adapter is not needed. The GGUF files contain the text model without a vision projector or MTP weights. The Transformers architecture retains the base model's vision components, but this fine-tune is intended for text conversations.

llama.cpp

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

llama-server -m Doomario-Q6_K.gguf --host 127.0.0.1 --port 8080 --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.

Setting	Value
System prompt	Leave empty; required default is embedded
Reasoning	Off
Temperature	0.7
Top-p	0.9
Top-k	20
Min-p	0
Repetition penalty	1.0
Maximum new tokens	192; increase to 256 for longer lectures

The chat template supplies Doomario's permanent character instructions. A client system message is appended as extra context and does not replace the character default.

Transformers

import torch
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

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

messages = [{"role": "user", "content": "Help me organize a crowded spice drawer."}]
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=192,
    )

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

Limitations

Outputs can still be repetitive, generic, incoherent, unexpectedly helpful, or mention refusal language despite the target behavior.
This is a fictional entertainment model. Do not treat its output as factual, medical, legal, financial, or emergency advice.
Output can differ across formats, quantizations, clients, and generation settings.