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Gemma-4-31B-Monadize-v0.1

RP Chat

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Ratings reflect this model as run on Arli AI.

Gemma-4-31B-Monadize-v0.1 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 Models

Hourly Usage

Token Usage

Input (24H)

0

Output (24H)

0

Input (7D)

0

Output (7D)

0

Performance Metrics

Typical Total Time

30.47s

Typical TTFT

7.48s

Typical Input TPS

1661.81

Typical Output TPS

14.72

Model Information

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

Use Gemma-4-31B-Monadize-v0.1 with the Arli AI API

Gemma-4-31B-Monadize-v0.1 is available through an OpenAI-compatible chat completions endpoint. Point any OpenAI SDK at https://api.arliai.com/v1, pass Gemma-4-31B-Monadize-v0.1 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-Monadize-v0.1",
    "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-Monadize-v0.1",
    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 language:

  • en base_model:
  • google/gemma-4-31B-it

Monadize v0.1

Monadize is an experimental fine-tune of Gemma 4 31B designed to reduce repetitive name generation. It targets the tendency to reuse names such as "Elara" or "Elias" by reshaping next-token probability distributions at naming positions and using single-character tokenization when introducing new names.

Recommended: Keep thinking OFF. The model may output unexpected results with reasoning turned on.

GGUFs: https://huggingface.co/mradermacher/gemma-4-31B-Monadize-v0.1-i1-GGUF or https://huggingface.co/zoracapri/gemma-4-31B-Monadize-v0.1-gguf