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

Libraries

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

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

pipe = pipeline("text-generation", model="MaralGPT/MaralGPT-Mythos-9B-2606-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MaralGPT/MaralGPT-Mythos-9B-2606-GGUF", dtype="auto")

llama-cpp-python

How to use MaralGPT/MaralGPT-Mythos-9B-2606-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MaralGPT/MaralGPT-Mythos-9B-2606-GGUF",
	filename="MaralGPT-Mythos-9B-2606-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MaralGPT/MaralGPT-Mythos-9B-2606-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 MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M

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 MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M

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 MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M

LM Studio
Jan

vLLM

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

Use Docker

docker model run hf.co/MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M

SGLang

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

Ollama
How to use MaralGPT/MaralGPT-Mythos-9B-2606-GGUF with Ollama:
```
ollama run hf.co/MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M
```

Unsloth Studio

How to use MaralGPT/MaralGPT-Mythos-9B-2606-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MaralGPT/MaralGPT-Mythos-9B-2606-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MaralGPT/MaralGPT-Mythos-9B-2606-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MaralGPT/MaralGPT-Mythos-9B-2606-GGUF to start chatting

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MaralGPT/MaralGPT-Mythos-9B-2606-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 MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M

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 MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M

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 "MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M" \
  --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"

Docker Model Runner
How to use MaralGPT/MaralGPT-Mythos-9B-2606-GGUF with Docker Model Runner:
```
docker model run hf.co/MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MaralGPT-Mythos-9B-2606-GGUF-Q4_K_M

List all available models

lemonade list

MaralGPT-Mythos-9B-2606-GGUF

File size: 3,054 Bytes

---
license: apache-2.0
base_model:
- Qwen/Qwen3.5-9B
- MaralGPT/MaralGPT-Mythos-9B-2606
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- qwen3.5
- reasoning
- uncensored
- long-context
- 1M-context
- function-calling
- tool-use
- sft
- full-fine-tune
- cybersecurity
- biomedical
- agentic
- heretic
- uncensored
- decensored
- abliterated
- reproducible
---

# MaralGPT Mythos 9B 2606 Edition 

<p align="center">
  <img src="maral-mythos-logo.png" width=768 height=768 />
</p>

## Quantization/GGUF Files 

| Quantization | Notes |
|:-------:|:--------------------:|
| `bf16`  | Original quantization|
| `Q8_0`  | 8-bits, perfect for gaming systems |
| `Q4_K_M` | 4-bits, good but can be sketchy |
| `Q2_K`  | 2-bits, does not work properly |

## How to run (Ollama)

Imagine you want to run 8 bit version just do this:

```
ollama run hf.co/MaralGPT/MaralGPT-Mythos-9B-2606-GGUF:Q8_0 --verbose
```

And it will be downloaded and executed on your computer. 

## 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](https://github.com/p-e-w/heretic) 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: 

```python
import torch
from transformers import AutoModelForImageTextToText, AutoTokenizer

model_id = "MaralGPT/MaralGPT-Mythos-9B-2606"
tokenizer = AutoTokenizer.from_pretrained(model_id)
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))
```

## Benchmarks 

### Generic Benchmark

<p align="center">
  <img src="benchmark-1.jpg" />
</p>

Above benchmark has been done on model parameters of: 

```
temperature=0.6 top_p=0.95 top_k=20
```

And change in those values may change the results accordingly. 

### Detailed Benchmark 

<p align="center">
  <img src="benchmark-2.jpg" />
</p>