Instructions to use aimeri/spoomplesmaxx-gemma4-31B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aimeri/spoomplesmaxx-gemma4-31B with Transformers:

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

pipe = pipeline("image-text-to-text", model="aimeri/spoomplesmaxx-gemma4-31B")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("aimeri/spoomplesmaxx-gemma4-31B")
model = AutoModelForImageTextToText.from_pretrained("aimeri/spoomplesmaxx-gemma4-31B")
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

vLLM

How to use aimeri/spoomplesmaxx-gemma4-31B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aimeri/spoomplesmaxx-gemma4-31B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aimeri/spoomplesmaxx-gemma4-31B",
		"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/aimeri/spoomplesmaxx-gemma4-31B

SGLang

How to use aimeri/spoomplesmaxx-gemma4-31B 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 "aimeri/spoomplesmaxx-gemma4-31B" \
    --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": "aimeri/spoomplesmaxx-gemma4-31B",
		"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 "aimeri/spoomplesmaxx-gemma4-31B" \
        --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": "aimeri/spoomplesmaxx-gemma4-31B",
		"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 aimeri/spoomplesmaxx-gemma4-31B with Docker Model Runner:
```
docker model run hf.co/aimeri/spoomplesmaxx-gemma4-31B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SpoomplesMaxx-V1.0-31b

⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣤⠴⠒⢒⣒⠲⠶⠤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⣠⣄⡖⢲⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠤⠴⠖⠒⠲⠦⡤⢤⡀⠀⢸⣿⠓⠋⠁⠲⢉⠙⠙⢳⣬⡓⢤⠀⠀⠀⡄⠀⠀⠀⠀⠀⠀⠀
⠀⠀⢧⡈⢀⡞⠁⠀⠀⠀⠀⠀⠀⠀⢀⡤⠖⠋⠀⠀⠀⠀⠀⠀⠈⠭⠵⠏⣷⢿⣿⠀⠀⠀⠠⠀⠀⠀⡈⠹⣿⡌⣳⠄⠀⡎⢀⠜⠁⠀⠀⠀⠀
⠀⠀⠀⠈⠉⠀⠀⢦⣀⠀⠀⠀⢀⣼⠃⠀⠀⠀⠀⠀⠀⠀⢀⣀⠀⠠⣀⢲⠀⣼⣾⠀⠀⣰⣿⣿⣷⠀⠀⠈⠹⠃⡹⡆⠈⠈⡡⠤⠖⠂⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠁⠀⢈⡟⠀⠀⠀⠀⠀⠀⠀⣴⣿⣿⣶⡌⣪⣼⠶⣿⡏⢣⠀⢿⣿⡿⠇⠀⠀⠀⠀⠉⠁⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡟⠀⠀⠀⠀⠀⠀⠀⠀⠙⠻⠛⠛⠰⣿⠁⠀⢹⢇⡞⠄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣇⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣴⣿⣧⢤⠄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⢦⣠⡿⠏⠀⠀⠀⢀⠀⠀⠈⠀⠀⢀⠀⠀⠀⣿⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⢀⣾⡟⠁⣠⣄⣀⣠⣤⡀⠀⠀⠀⠀⠀⠀⠀⠠⢀⢸⡟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⣿⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⡾⣣⣄⡜⠛⠫⡛⢹⠟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢿⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⡈⣿⠁⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⣸⣵⠋⠋⠁⠀⠈⠡⠚⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢿⢽⣾⣿⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⡿⠉⢁⢀⠀⢠⣨⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡏⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⣿⣿⡏⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⣸⢁⡉⡗⡗⣸⣿⠟⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣼⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⣿⠁⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⡟⡼⣱⢻⢧⣿⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⣿⠀⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⢰⣿⢱⣿⣿⡿⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⣿⣿⣴⣿⡶⠀⠀⠒⣤⠠⣤⠀⠐⠂⠀⠀⡰⠃⢀⣀⣀⣀⣀⡤⠀⠀⠀⠀
⠀⠀⠀⠀⠀⢸⣿⢸⣿⣿⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⡠⢸⣿⣿⣿⢿⣿⣷⣤⣤⣀⣤⢶⢶⣶⠼⠷⢾⡟⠉⡉⣨⣏⠓⡒⢲⣖⣒⣀⣀⡀
⠀⠀⠀⠀⠀⢸⣿⣾⣿⠉⠀⠠⢸⢤⣾⣶⣶⣿⣷⣴⢺⣿⣷⣿⣭⣴⣶⣦⣿⣿⣷⣿⣿⡿⢿⣿⣻⣶⠿⠚⠛⠛⠋⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉
⠀⠀⠀⠀⠀⢸⣿⣿⣇⡨⠄⠤⡖⢛⣛⣿⣿⣶⣿⣿⡿⣿⣿⣿⣿⣿⡏⠛⠛⢋⣩⢶⡟⠑⣠⡿⠛⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⢀⣀⣠⠤⣶⣺⣯⣷⣶⣾⣿⣯⣷⡶⠿⣿⣻⠂⣠⠟⠁⢸⣿⣵⣿⡼⣿⣴⣰⣿⣣⢞⣴⡾⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠈⠉⢁⣿⣿⣿⣿⣿⣿⡿⠉⠁⠁⠀⢀⡼⣡⠞⠁⠀⠀⠀⢻⣽⣯⠾⣥⣿⣿⣿⣿⠟⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠘⠛⠉⠁⢸⣿⣸⣿⠀⠀⠐⢀⡴⣋⠞⠁⠀⠀⠀⠀⠀⣿⣿⣿⢾⠿⠋⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⢸⣿⡟⡏⠀⣠⣴⣫⡾⠁⠀⠀⠀⠀⠀⠀⠀⠀⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⢸⡟⡇⣍⣨⢵⡿⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⢿⣹⡥⠞⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀

SpoomplesMaxx is a generalist model with primary strengths in creative writing and roleplay, plus working-grade competence at instruction following, tool use, and reasoning.

Built on Gemma 4 31B with continued pretraining on an English and Portuguese-heavy corpus, then run through an SFT pipeline covering instruction tuning, tool calling, reasoning, and a narrative persona (Olivia).

Pre-release snapshot

This is an early snapshot of an in-progress training run, published for testing and feedback. A GRPO stage is planned to reinforce consistent application of the reasoning format learned during SFT. Expect rough edges; capabilities will continue shifting in later releases.

Key Details

BASE MODEL: google/gemma-4-31b
LICENSE:    TBD
LANGUAGES:  English & Portuguese (reasoning traces); multilingual via base

Sampling

Use the defaults in generation_config.json.

Alignment

No RLHF or safety alignment has been applied beyond what exists in the base model. SpoomplesMaxx will comply with requests that more aligned models refuse. Use accordingly.

Downloads last month: 224

Safetensors

Model size

33B params

Tensor type

BF16

Model tree for aimeri/spoomplesmaxx-gemma4-31B

Base model

aimeri/spoomplesmaxx-base-gemma4-31b

Finetuned

(1)

this model

Quantizations

4 models