RP Chat
Ratings reflect this model as run on Arli AI.
Gemma-4-31B-Storymaxxed is a text generation model available on Arli AI. It offers 31B parameters, 262K token context, reasoning support, vision input. Recommended use: RP Chat. Run it through the OpenAI-compatible chat completions API with unlimited requests and zero-log privacy.
View on Hugging FaceBack to ModelsInput (24H)
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Output (24H)
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Input (7D)
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Output (7D)
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Typical Total Time
30.47s
Typical TTFT
7.48s
Typical Input TPS
1661.81
Typical Output TPS
14.72
Context Size
262144
Quantization
r128 on FP8
Engine
vllm
Creation Method
Finetune
Model Class
Gemma-4-31B - LoRA
Chat Template
Gemma4
Reasoning
Yes
Vision
Yes
Parameters
31B
Added At
10/31/2026
Gemma-4-31B-Storymaxxed is available through an OpenAI-compatible chat completions endpoint. Point any OpenAI SDK at https://api.arliai.com/v1, pass Gemma-4-31B-Storymaxxed as the model, and requests are served without logging.
cURL
curl https://api.arliai.com/v1/chat/completions \
-H "Authorization: Bearer $ARLIAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "Gemma-4-31B-Storymaxxed",
"messages": [{"role": "user", "content": "Hello!"}]
}'Python
from openai import OpenAI
client = OpenAI(
base_url="https://api.arliai.com/v1",
api_key="YOUR_ARLIAI_API_KEY",
)
response = client.chat.completions.create(
model="Gemma-4-31B-Storymaxxed",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)See the text generation docs for every parameter, or read the quick start guide to make your first request.
license: apache-2.0 base_model: trohrbaugh/gemma-4-31b-it-heretic-ara tags:
This model is a fine-tuned version of trohrbaugh/gemma-4-31b-it-heretic-ara. It has been trained using TRL. Optimized specifically for long-form creative storywriting and narrative prose.
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
This model was trained with TRL using DPO on a high quality dataset of narrative preference pairs. It was LoRa trained on over 5,000 pairs for 8 hours.
Introduction to training method used: Direct Preference Optimization: Your Language Model is Secretly a Reward Model.
For optimal inference, use the standard generation parameters recommended by Google for Gemma-4 models:
The mmproj file for vision can be found here: https://huggingface.co/MRockatansky/Gemma-4-31B-storymaxxed-GGUF
Range of quants courtesy of mradermacher: https://huggingface.co/mradermacher/Gemma-4-31B-storymaxxed-GGUF
Cite DPO as:
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
Cite TRL as:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}