Instructions to use MaralGPT/MaralGPT-Mythos-9B-2606 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaralGPT/MaralGPT-Mythos-9B-2606 with Transformers:

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

pipe = pipeline("text-generation", model="MaralGPT/MaralGPT-Mythos-9B-2606")
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("MaralGPT/MaralGPT-Mythos-9B-2606")
model = AutoModelForMultimodalLM.from_pretrained("MaralGPT/MaralGPT-Mythos-9B-2606")
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

vLLM

How to use MaralGPT/MaralGPT-Mythos-9B-2606 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaralGPT/MaralGPT-Mythos-9B-2606"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaralGPT/MaralGPT-Mythos-9B-2606",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaralGPT/MaralGPT-Mythos-9B-2606

SGLang

How to use MaralGPT/MaralGPT-Mythos-9B-2606 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 "MaralGPT/MaralGPT-Mythos-9B-2606" \
    --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": "MaralGPT/MaralGPT-Mythos-9B-2606",
		"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 "MaralGPT/MaralGPT-Mythos-9B-2606" \
        --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": "MaralGPT/MaralGPT-Mythos-9B-2606",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaralGPT/MaralGPT-Mythos-9B-2606 with Docker Model Runner:
```
docker model run hf.co/MaralGPT/MaralGPT-Mythos-9B-2606
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MaralGPT Mythos 9B 2606 Edition

What is this model?

This model is an uncensored finetuned version of Qwen 3.5 with nine billion parameters which can be executed on pretty much any gaming systems. The data of this model was over 500 million tokens of synthetic data generated by state-of-the-art models such as GPT 5.5 or Claude 4.8 Opus and as long as we had access, Claude 5 Fable.

All so-called ethical barriers removed from the model using Heretic LLM library to make it a suitable tool for cybersecurity, biology and chemistry. You can easily ask anything you want from this model and it will answer without any censorship.

Key Features

📝 Context window of over one million tokens.
🔞 Uncensored answers
♾️ Good at math, physics, chemistry, etc.
💻 Can be executed on a gaming laptop

How to run

First, install needed libraries:

pip install transformers accelerate

Then:

import torch
from transformers import AutoModelForImageTextToText, AutoTokenizer

model_id = "MaralGPT/MaralGPT-Mythos-9B-2606"
tokenizer = AutoTokenizer.from_pretrained("haghiri")
model = AutoModelForImageTextToText.from_pretrained(
    model_id, dtype="bfloat16", device_map="cuda"
)

messages = [
    {"role": "user",
     "content": "Write a simple snake game in python."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

out = model.generate(
    **inputs, max_new_tokens=16384, do_sample=True,
    temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05,
)

print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))