Instructions to use OmniJev/OneJev-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OmniJev/OneJev-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OmniJev/OneJev-4B")
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("OmniJev/OneJev-4B")
model = AutoModelForMultimodalLM.from_pretrained("OmniJev/OneJev-4B", 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 OmniJev/OneJev-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OmniJev/OneJev-4B"
# 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/OneJev-4B",
		"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/OneJev-4B

SGLang

How to use OmniJev/OneJev-4B 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/OneJev-4B" \
    --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/OneJev-4B",
		"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/OneJev-4B" \
        --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/OneJev-4B",
		"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/OneJev-4B with Docker Model Runner:
```
docker model run hf.co/OmniJev/OneJev-4B
```

BradNLP commited on 3 days ago

Commit

c88e186

verified ·

1 Parent(s): d296ce4

update quick start

Browse files

Files changed (1) hide show

README.md +10 -1

README.md CHANGED Viewed

@@ -43,7 +43,7 @@ On one H200 with a 1280x720 screenshot, OneJev-4B answers 1 question in 64 ms an
 ## Quick start
 ```bash
-pip install git+https://github.com/OmniJev/OneJev.git
 qev serve --model OmniJev/OneJev-4B
 ```
@@ -62,6 +62,15 @@ r = Client("http://localhost:8000").system_one(
 The server speaks TypeSafe's System One API plus a `media` field for images and video. More examples, the latency
 benchmark and the code are on [GitHub](https://github.com/OmniJev/OneJev).
 ## All sizes
 | Model | Base | Weights |

 ## Quick start
 ```bash
+pip install "qev[torch] @ git+https://github.com/OmniJev/OneJev.git"
 qev serve --model OmniJev/OneJev-4B
 ```
 The server speaks TypeSafe's System One API plus a `media` field for images and video. More examples, the latency
 benchmark and the code are on [GitHub](https://github.com/OmniJev/OneJev).
+OneJev-4B also runs on llama.cpp from the [GGUF build](https://huggingface.co/mradermacher/OneJev-4B-GGUF) by mradermacher,
+without PyTorch. Images work there; video needs the PyTorch server.
+```bash
+brew install llama.cpp
+pip install git+https://github.com/OmniJev/OneJev.git
+qev serve --gguf mradermacher/OneJev-4B-GGUF:Q8_0
+```
 ## All sizes
 | Model | Base | Weights |