GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
MaralGPT
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
MaralGPT
Model Tree
Qwen/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
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.
First, install needed libraries:
text
pip install transformers accelerateThen:
python
import torchfrom 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))Above benchmark has been done on model parameters of:
text
temperature=0.6 top_p=0.95 top_k=20And change in those values may change the results accordingly.
import torchfrom 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))