Instructions to use godx7/SAMTok-self-contained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use godx7/SAMTok-self-contained with Transformers:

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

pipe = pipeline("image-text-to-text", model="godx7/SAMTok-self-contained", trust_remote_code=True)
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 AutoModel
model = AutoModel.from_pretrained("godx7/SAMTok-self-contained", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use godx7/SAMTok-self-contained with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "godx7/SAMTok-self-contained"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "godx7/SAMTok-self-contained",
		"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/godx7/SAMTok-self-contained

SGLang

How to use godx7/SAMTok-self-contained 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 "godx7/SAMTok-self-contained" \
    --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": "godx7/SAMTok-self-contained",
		"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 "godx7/SAMTok-self-contained" \
        --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": "godx7/SAMTok-self-contained",
		"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 godx7/SAMTok-self-contained with Docker Model Runner:
```
docker model run hf.co/godx7/SAMTok-self-contained
```

SAMTok-self-contained

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# Qwen3-VL-4B-SAMTok Composite

This directory is a self-contained Hugging Face remote-code model wrapper around
Qwen3-VL-4B-SAMTok and the SAMTok mask decoder.

```python
from transformers import AutoModel

model = AutoModel.from_pretrained(
    "your-org/Qwen3-VL-4B-SAMTok-Composite",
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto",
)

result = model.generate_with_masks(
    image="example.jpg",
    question="Please describe the image with interleaved segmentation masks.",
)
print(result["text"])
print(result["masks"].shape if result["masks"] is not None else None)
```

The original Qwen3-VL config is stored as `qwen_config.json`; `config.json`
registers the composite `AutoModel` remote code.