Instructions to use maldv/Gemma-4-31B-Isometry-RP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maldv/Gemma-4-31B-Isometry-RP with Transformers:

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

pipe = pipeline("text-generation", model="maldv/Gemma-4-31B-Isometry-RP")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("maldv/Gemma-4-31B-Isometry-RP")
model = AutoModelForMultimodalLM.from_pretrained("maldv/Gemma-4-31B-Isometry-RP", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use maldv/Gemma-4-31B-Isometry-RP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maldv/Gemma-4-31B-Isometry-RP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maldv/Gemma-4-31B-Isometry-RP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maldv/Gemma-4-31B-Isometry-RP

SGLang

How to use maldv/Gemma-4-31B-Isometry-RP 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 "maldv/Gemma-4-31B-Isometry-RP" \
    --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": "maldv/Gemma-4-31B-Isometry-RP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "maldv/Gemma-4-31B-Isometry-RP" \
        --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": "maldv/Gemma-4-31B-Isometry-RP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maldv/Gemma-4-31B-Isometry-RP with Docker Model Runner:
```
docker model run hf.co/maldv/Gemma-4-31B-Isometry-RP
```

Gemma-4-31B-Isometry-RP / tokenizer_config.json

maldv

Upload folder using huggingface_hub

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	{
	"audio_token": "<\|audio\|>",
	"backend": "tokenizers",
	"boa_token": "<\|audio>",
	"boi_token": "<\|image>",
	"bos_token": "<bos>",
	"eoa_token": "<audio\|>",
	"eoc_token": "<channel\|>",
	"eoi_token": "<image\|>",
	"eos_token": "<eos>",
	"eot_token": "<turn\|>",
	"escape_token": "<\|\"\|>",
	"etc_token": "<tool_call\|>",
	"etd_token": "<tool\|>",
	"etr_token": "<tool_response\|>",
	"extra_special_tokens": [
	"<\|video\|>"
	],
	"image_token": "<\|image\|>",
	"mask_token": "<mask>",
	"model_max_length": 1000000000000000019884624838656,
	"pad_token": "<pad>",
	"padding_side": "left",
	"processor_class": "Gemma4Processor",
	"response_schema": {
	"type": "object",
	"properties": {
	"role": {
	"const": "assistant"
	},
	"thinking": {
	"type": "string"
	},
	"content": {
	"type": "string"
	},
	"tool_calls": {
	"x-regex-iterator": "<\\\|tool_call>(.*?)<tool_call\\\|>",
	"type": "array",
	"items": {
	"type": "object",
	"properties": {
	"type": {
	"const": "function"
	},
	"function": {
	"type": "object",
	"x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})",
	"properties": {
	"name": {
	"type": "string"
	},
	"arguments": {
	"type": "object",
	"x-parser": "gemma4-tool-call",
	"additionalProperties": {}
	}
	}
	}
	}
	}
	}
	},
	"x-regex": "(\\<\\\|channel\\>thought\\n(?P<thinking>.?)\\<channel\\\|\\>)?(?P<tool_calls>\\<\\\|tool_call\\>.\\<tool_call\\\|\\>)?(?P<content>(?:(?!\\<turn\\\|\\>)(?!\\<\\\|tool_response\\>).)+)?(?:\\<turn\\\|\\>\|\\<\\\|tool_response\\>)?"
	},
	"soc_token": "<\|channel>",
	"sot_token": "<\|turn>",
	"stc_token": "<\|tool_call>",
	"std_token": "<\|tool>",
	"str_token": "<\|tool_response>",
	"think_token": "<\|think\|>",
	"tokenizer_class": "GemmaTokenizer",
	"unk_token": "<unk>"
	}