Image-to-Text
PEFT
Safetensors
Portuguese
English
vision-language
table-extraction
scientific-figures
markdown-table
lightonocr
qwen2.5-vl
qwen3-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
Commit History
12 days ago
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Update README with LightOnOCR-3-4B (0.5232) and Qwen3-VL-8B (0.4872) benchmark results
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docs: remove metrics below ICDAR Score Composto from test benchmark table
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docs: add Qwen2.5-VL-7B SOTA results column and ICDAR leaderboard ranking to model card
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docs: atualiza metricas finais oficiais do conjunto de teste (373 figuras) e tempo de treino
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feat: upload qwen2.5-vl-3b native original resolution weights and pt-br model card
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docs: adiciona tempo de treinamento e hardware a tabela comparativa
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docs: atualiza explicacao detalhada da loss icdar vs padrao e remove emojis
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docs: update pt-br readme with detailed loss explanation, lora mechanics and +pp metrics
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feat: initial release of sci-image-markdown Qwen2.5-VL-3B LoRA adapter and extraction codebase
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