Transformers
Safetensors
vision-language
document-understanding
boundingdocs
bitsandbytes
8-bit precision
Instructions to use CompressingVLM/glm-ocr-boundingdocs-ft-bnb-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CompressingVLM/glm-ocr-boundingdocs-ft-bnb-int8 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CompressingVLM/glm-ocr-boundingdocs-ft-bnb-int8", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44b0705cfb9112cfa77bc2904bbd221462e563936ee1f0c4d1034539a8377f3e
- Size of remote file:
- 1.31 GB
- SHA256:
- 5f811dbeeb6f065fe47442c60a0920e61903f82ccb2828bb218402181d8e7d23
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