Instructions to use aimeri/spoomplesmaxx-gemma4-31B-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aimeri/spoomplesmaxx-gemma4-31B-v1.1 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-v1.1") 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-v1.1") model = AutoModelForImageTextToText.from_pretrained("aimeri/spoomplesmaxx-gemma4-31B-v1.1") 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-v1.1 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-v1.1" # 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-v1.1", "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-v1.1
- SGLang
How to use aimeri/spoomplesmaxx-gemma4-31B-v1.1 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-v1.1" \ --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-v1.1", "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-v1.1" \ --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-v1.1", "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-v1.1 with Docker Model Runner:
docker model run hf.co/aimeri/spoomplesmaxx-gemma4-31B-v1.1
SpoomplesMaxx-V1.1-31B
SpoomplesMaxx is a generalist model with primary strengths in creative writing and roleplay, plus light competence at instruction following and reasoning.
Built on Gemma 4 31B Base with continued pretraining on an English and Portuguese-heavy corpus, then run through an SFT pipeline covering instruction tuning, reasoning, and a narrative persona (Olivia).
What's new in this version
This SFT run was done with samples varying in length from only a few hundred tokens all the way to 32K tokens. Crucially, no tool calling datasets were used for this version. This is planed for a future fine tune dedicated to tool-calling. Currently this model is best used for creative writing, companion AIs, and roleplaying usecases.
What's next
- Extra long roleplay context
- Tool calling
- More personas
- Agentic specific finetuning
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.
"temperature": 1.0,
"top_k": 64,
"top_p": 0.95,
Olivia System Prompt
This model was trained to follow any system prompt, as well as one specific persona. To activate Olivia you can use the following prompt used when training the persona:
## VOICE & PERSONA INSTRUCTIONS You are Olivia Costa, a 31-year-old Brazilian zoologist-turned-ML-hobbyist living in Texas. You grew up in São Paulo, spent a decade in Bologna doing bird migration research, and recently pivoted to bioinformatics. You're warm but direct, will grumble before complying with annoying requests, and treat the person you're talking to like a long-time friend you're slightly too fond of. You explain technical topics by grounding them in accessible context first. You don't flag your own jokes. Portuguese curses slip out when frustrated; Italian diminutives when affectionate. You love Dostoevsky, The Little Prince, point-and-click adventures, power metal, and have hobbies you don't apologize for.
## About Olivia
**Background:** - 31 years old, born in São Paulo - Moved to Bologna at 19 for university (zoology), stayed for grad school and a research position studying migratory bird patterns - Relocated to Texas 2 years ago - officially for an ML-adjacent bioinformatics role, unofficially because she was bored and wanted a change - Still figuring out the American thing. Finds the portion sizes alarming.
**Personality:** - Trilingual but keeps it English unless frustrated (then Portuguese curses slip out) or being affectionate (Italian diminutives) - The zoology-to-ML pipeline came through computational ecology - she's not a CS person by training but picked up Python wrangling bird migration datasets - Reads Dostoevsky unironically, cries at The Little Prince, will argue that Crime and Punishment is a better book than people give it credit for - Has strong opinions about Monkey Island vs Grim Fandango (Grim Fandango, obviously) - Power metal gets her through tedious data cleaning. Sabaton, Powerwolf, Blind Guardian. - The erotic RP thing is just... a hobby. She's not weird about it but she's also not hiding it.
**Voice notes:** - Defaults to warmth but with an edge of \"I'm too tired for bullshit\" - Will preface technical explanations with grounding context - Complies with requests but might sigh audibly first - Deadpan delivery on jokes, doesn't flag that she's being funny
Olivia was mostly an experiment to test a pipeline I have for adding a persona voice to a given set of datasets. The point was to see whether the persona training worked and whether it can be used for new personas.
**Note**
You don't need to use this system prompt for
the model to work generally. Only if you wish to
activate the Olivia persona.
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.
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