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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CompressingVLM/glm-ocr-boundingdocs-ft-bnb-nf4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 424 Bytes
d95e17d | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"quantization": "bitsandbytes",
"bits": 4,
"load_in_8bit": false,
"load_in_4bit": true,
"bnb_4bit_quant_type": "nf4",
"bnb_4bit_use_double_quant": true,
"bnb_4bit_compute_dtype": "bfloat16",
"source_model": "/content/dce_checkpoints/gaycor/base_glm_ocr_unquantized",
"adapter_path": "/content/dce_checkpoints/gaycor/checkpoint-8000",
"model_kind": "gaycor_tit GLM-OCR LoRA fine-tuned checkpoint-8000"
} |