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

9bbf292 verified 2 months ago

preview code

Raw

History Blame

2.11 kB

	---
	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](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [MRockatansky/Gemma-4-31B-storymaxxed2](https://huggingface.co/MRockatansky/Gemma-4-31B-storymaxxed2) as a base.

	### Models Merged

	The following models were included in the merge:
	* [Gryphe/Pantheon-Reasoning-31B-1.1](https://huggingface.co/Gryphe/Pantheon-Reasoning-31B-1.1)
	* [zerofata/G4-MeroMero-31B](https://huggingface.co/zerofata/G4-MeroMero-31B)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	# 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
	```