Instructions to use MRockatansky/gemma-4-31B-Mergemaxxed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MRockatansky/gemma-4-31B-Mergemaxxed with Transformers:

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

pipe = pipeline("image-text-to-text", model="MRockatansky/gemma-4-31B-Mergemaxxed")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("MRockatansky/gemma-4-31B-Mergemaxxed")
model = AutoModelForMultimodalLM.from_pretrained("MRockatansky/gemma-4-31B-Mergemaxxed", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MRockatansky/gemma-4-31B-Mergemaxxed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MRockatansky/gemma-4-31B-Mergemaxxed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MRockatansky/gemma-4-31B-Mergemaxxed",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/MRockatansky/gemma-4-31B-Mergemaxxed

SGLang

How to use MRockatansky/gemma-4-31B-Mergemaxxed 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 "MRockatansky/gemma-4-31B-Mergemaxxed" \
    --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": "MRockatansky/gemma-4-31B-Mergemaxxed",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "MRockatansky/gemma-4-31B-Mergemaxxed" \
        --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": "MRockatansky/gemma-4-31B-Mergemaxxed",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use MRockatansky/gemma-4-31B-Mergemaxxed with Docker Model Runner:
```
docker model run hf.co/MRockatansky/gemma-4-31B-Mergemaxxed
```

gemma-4-31B-Mergemaxxed / README.md

MRockatansky

Upload folder using huggingface_hub

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base_model:
  - MRockatansky/Gemma-4-31B-storymaxxed2
  - Gryphe/Pantheon-Reasoning-31B-1.1
  - zerofata/G4-MeroMero-31B
library_name: transformers
tags:
  - mergekit
  - merge

mergemaxxed

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using MRockatansky/Gemma-4-31B-storymaxxed2 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

# Model Merge Configuration for Creative Writing & Roleplay Gemma-4
# Combines: Storymaxxed (story writing), MeroMero (roleplay), Pantheon (reasoning)
# Merge Method: DARE TIES - Best for 3+ specialized models with distinct capabilities

merge_method: dare_ties

base_model: MRockatansky/Gemma-4-31B-storymaxxed2  # Using Storymaxxed as base

models:
  # Primary: Story Writing Specialist
  - model: MRockatansky/Gemma-4-31B-storymaxxed2
    parameters:
      weight: 0.4
      density: 0.7  # Keep 70% of task vector parameters

  # Secondary: Creative Roleplay Specialist  
  - model: zerofata/G4-MeroMero-31B
    parameters:
      weight: 0.3
      density: 0.7  # Keep 70% of task vector parameters

  # Tertiary: Reasoning Enhancement
  - model: Gryphe/Pantheon-Reasoning-31B-1.1
    parameters:
      weight: 0.3
      density: 0.7  # Keep 70% of task vector parameters

parameters:
  # Normalize weights across models
  normalize: true
  
  # Use int8 masks for memory efficiency (important for 31B models)
  int8_mask: true

# Output precision - bfloat16 recommended for Gemma-4
dtype: bfloat16

# Optional: Tokenizer configuration
# Using union to preserve any special tokens from all models
tokenizer:
  source: union