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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CompressingVLM/glm-ocr-boundingdocs-ft-bnb-int8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 741 Bytes
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library_name: transformers
tags:
- vision-language
- document-understanding
- boundingdocs
- bitsandbytes
- 8-bit
---
# CompressingVLM/glm-ocr-boundingdocs-ft-bnb-int8
Source model: `/content/dce_checkpoints/gaycor/base_glm_ocr_unquantized`
Quantization: bitsandbytes 8-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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