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
File size: 630 Bytes
be90b31 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | """
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
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