Image-to-Text
PEFT
Safetensors
Portuguese
English
vision-language
table-extraction
scientific-figures
markdown-table
qwen2.5-vl
lora
icdar-metric-loss
Instructions to use lucasoc/sci-image-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lucasoc/sci-image-models with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct") model = PeftModel.from_pretrained(base_model, "lucasoc/sci-image-models") - Notebooks
- Google Colab
- Kaggle
Download src/utils/logging.py from lucasoc/sci-image-models: direct link, hf CLI and curl.
- Browser
- Download file 630 Bytes
-
https://huggingface.co/lucasoc/sci-image-models/resolve/main/src/utils/logging.py
- Command line
-
hf download hf://lucasoc/sci-image-models/src/utils/logging.py
-
curl -L -o logging.py https://huggingface.co/lucasoc/sci-image-models/resolve/main/src/utils/logging.py
630 Bytes
| """ | |
| Logging setup using Python logging and Rich. | |
| """ | |
| import logging | |
| import sys | |
| from rich.logging import RichHandler | |
| def setup_logger(name: str = "sci-image-markdown", level: int = logging.INFO) -> logging.Logger: | |
| """Configures and returns a rich logger.""" | |
| logger = logging.getLogger(name) | |
| logger.setLevel(level) | |
| if not logger.handlers: | |
| handler = RichHandler( | |
| rich_tracebacks=True, | |
| markup=True, | |
| show_time=True, | |
| show_path=False | |
| ) | |
| handler.setFormatter(logging.Formatter("%(message)s")) | |
| logger.addHandler(handler) | |
| return logger | |