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
CompressingVLM/glm-ocr-boundingdocs-ft-bnb-nf4
Source model: /content/dce_checkpoints/gaycor/base_glm_ocr_unquantized
Quantization: bitsandbytes 4-bit.
Model kind: gaycor_tit GLM-OCR LoRA fine-tuned checkpoint-8000
This repository stores a PEFT adapter plus quantization metadata. Load the base model with the included BitsAndBytesConfig and then apply the adapter.
The accompanying notebooks evaluate with the same branch metrics used before quantization:
ANLS, spatial precision/recall/F1, ANLS * Spatial F1, and bbox coverage. For the Qwen distilled model they also report value exact/contains match.
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