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
| 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. | |