Transformers
Safetensors
vision-language
document-understanding
boundingdocs
bitsandbytes
4-bit precision
Instructions to use CompressingVLM/glm-ocr-boundingdocs-ft-bnb-nf4 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-nf4 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CompressingVLM/glm-ocr-boundingdocs-ft-bnb-nf4", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03205aa6796a1b7050c48445b15053cd4b79db69236c40f12f0716f223bc227a
- Size of remote file:
- 867 MB
- SHA256:
- 573268594c5f23c000b8687a22e4d50d883e41248c8bbba88800d84ee489fb65
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