Image-Text-to-Text
Transformers
Safetensors
samtok_composite
feature-extraction
qwen3-vl
samtok
segmentation
remote-code
conversational
custom_code
Instructions to use godx7/SAMTok-self-contained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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, dtype="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
Qwen3-VL-4B-SAMTok Composite
This is a self-contained Hugging Face remote-code wrapper for Qwen3-VL-4B-SAMTok. It loads both the Qwen3-VL language-vision model and the SAMTok VQ-SAM2 mask decoder from this single model repository.
Usage
from transformers import AutoModel
model = AutoModel.from_pretrained(
"godx7/SAMTok-self-contained",
trust_remote_code=True,
torch_dtype="auto",
device_map="auto",
)
result = model.generate_with_masks(
image="example.jpg",
question=(
"Could you please give me a detailed description of the image? "
"Please respond with interleaved segmentation masks for the corresponding parts of the answer."
),
)
print(result["text"])
print(result["tags"])
print(result["masks"].shape if result["masks"] is not None else None)
For lower-level usage, the model forwards normal Qwen3-VL calls:
processor = model.processor
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=512)
You can also decode SAMTok text tokens directly:
masks, tags = model.decode_masks(output_text, image="example.jpg")
Files
config.json: composite remote-code config.qwen_config.json: original Qwen3-VL model config.model-*.safetensors: Qwen3-VL weights.mask_tokenizer_256x2.pth: SAMTok VQ decoder weights.sam2.1_hiera_large.pt: SAM2 image encoder checkpoint.modeling_samtok.py,configuration_samtok.py,samtok/: remote-code implementation.
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