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godx7/SAMTok-self-contained
SAMTok-self-contained is a image-text-to-text model from godx7. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers.
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.
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.safetensors9.7 GB · 84%
From the Hugging Face model README
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.
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")
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.