Image Segmentation
Transformers
Safetensors
qwen3_vl
image-text-to-text
pixvl
qwen3-vl
visual-grounding
Instructions to use HudeKing/PixVL-damgar-model-c-baseline-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HudeKing/PixVL-damgar-model-c-baseline-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="HudeKing/PixVL-damgar-model-c-baseline-sft")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("HudeKing/PixVL-damgar-model-c-baseline-sft") model = AutoModelForMultimodalLM.from_pretrained("HudeKing/PixVL-damgar-model-c-baseline-sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
PixVL-damgar-model-c-baseline-sft
Public PixVL training export from exports/damgar_chooseone_model_c_baseline_sft_8g_20260727. This repository contains the
complete Hugging Face model export. Source code and evaluation wrappers are
available at https://github.com/StuHude/PixVL.
The model is released for research use. It is initialized from Qwen3-VL-4B-SAMTok and follows the upstream model's license and usage restrictions.
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