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/data/__init__.py from lucasoc/sci-image-models: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/lucasoc/sci-image-models/resolve/main/src/data/__init__.py
- Command line
-
hf download hf://lucasoc/sci-image-models/src/data/__init__.py
-
curl -L -o __init__.py https://huggingface.co/lucasoc/sci-image-models/resolve/main/src/data/__init__.py
296 Bytes
| """ | |
| Dataset, preprocessor, and collation modules. | |
| """ | |
| from .dataset import SciImageTableDataset | |
| from .preprocessor import format_qwen_vl_conversation | |
| from .collator import QwenVLDataCollator | |
| __all__ = [ | |
| "SciImageTableDataset", | |
| "format_qwen_vl_conversation", | |
| "QwenVLDataCollator", | |
| ] | |