Instructions to use OmniJev/OneJev1.5g-31B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OmniJev/OneJev1.5g-31B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OmniJev/OneJev1.5g-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)

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

processor = AutoProcessor.from_pretrained("OmniJev/OneJev1.5g-31B")
model = AutoModelForMultimodalLM.from_pretrained("OmniJev/OneJev1.5g-31B", 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 OmniJev/OneJev1.5g-31B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OmniJev/OneJev1.5g-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": "OmniJev/OneJev1.5g-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/OmniJev/OneJev1.5g-31B

SGLang

How to use OmniJev/OneJev1.5g-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 "OmniJev/OneJev1.5g-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": "OmniJev/OneJev1.5g-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 "OmniJev/OneJev1.5g-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": "OmniJev/OneJev1.5g-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 OmniJev/OneJev1.5g-31B with Docker Model Runner:
```
docker model run hf.co/OmniJev/OneJev1.5g-31B
```

OneJev1.5g-31B is a multimodal System One decision model built on google/gemma-4-31B-it. Give it a screenshot, a photo or plain text along with a few typed questions, and it returns a calibrated probability for every option in a single forward pass.

Quick start

uv venv -p 3.12 && source .venv/bin/activate
uv pip install flide torchvision
flide serve --model OmniJev/OneJev1.5g-31B

In another terminal, run this example with your own screenshot.png:

IMG=$(base64 -w0 screenshot.png)
curl http://localhost:8000/v1/systemone -H "Content-Type: application/json" -d @- <<EOF
{
  "state": {"task": "Pay the open invoice from ACME", "screen": "<image:1>"},
  "media": [{"type": "image", "data": "data:image/png;base64,$IMG"}],
  "questions": {
    "done": {"type": "noul", "instructions": "The invoice has been paid"},
    "next": {"type": "choice", "instructions": "What should the agent do next?",
             "criteria": {"click": "click an element", "type": "type text", "scroll": "scroll", "stop": "stop"}},
    "progress": {"type": "score", "instructions": "How far along is the task?",
                 "criteria": ["not started", "halfway", "almost done", "done"]}
  }
}
EOF

answers.done.noul is the probability of yes, answers.next.probabilities gives one probability per option and answers.progress.score is the expected level.

Flide serves TypeSafe's System One API plus a media field for images. More examples are on GitHub.