MaralGPT-Mythos-9B-2606-GGUF

Quantization/GGUF Files

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 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(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

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