Image-to-Text
PEFT
Safetensors
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vision-language
table-extraction
scientific-figures
markdown-table
qwen2.5-vl
lora
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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: 917 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 27 28 29 30 | """
Configuration management utilities.
"""
import os
from typing import Any, Dict, Optional
import yaml
def load_config(config_path: str) -> Dict[str, Any]:
"""Load a YAML configuration file."""
if not os.path.exists(config_path):
raise FileNotFoundError(f"Configuration file not found: {config_path}")
with open(config_path, "r", encoding="utf-8") as f:
return yaml.safe_load(f) or {}
def merge_configs(base: Dict[str, Any], override: Optional[Dict[str, Any]]) -> Dict[str, Any]:
"""Deep merge two dictionaries, where override takes precedence."""
if not override:
return base.copy()
result = base.copy()
for key, value in override.items():
if key in result and isinstance(result[key], dict) and isinstance(value, dict):
result[key] = merge_configs(result[key], value)
else:
result[key] = value
return result
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