Image-to-Text
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
svg
hivg
vector-graphics
text-to-svg
image-to-svg
hierarchical-tokenization
autoregressive-generation
code-generation
text-generation-inference
Instructions to use xingxm/HiVG-3B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xingxm/HiVG-3B-Base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="xingxm/HiVG-3B-Base")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("xingxm/HiVG-3B-Base") model = AutoModelForImageTextToText.from_pretrained("xingxm/HiVG-3B-Base") - Notebooks
- Google Colab
- Kaggle
Add model card and metadata
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face team. I've updated the model card to include relevant metadata such as the pipeline tag and library name. I've also added links to the paper, GitHub repository, and project page, along with a sample usage snippet from the README to help users get started with the model.
Hi Niels, thank you so much for taking the time to improve the model card! The metadata updates (pipeline tag, library name) and the added links to the paper, GitHub repo, and project page are really helpful. I also appreciate the sample usage snippet β it will make it much easier for users to get started. Thanks again for the support from the Hugging Face team! π