Text Generation
PEFT
Safetensors
Romanian
English
relation-extraction
romanian
cross-lingual
qlora
lora
end-to-end
conversational
Instructions to use DS4AI-UPB/gemma4-ro-e2e-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DS4AI-UPB/gemma4-ro-e2e-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/export/home/acs/prof/dragos.vasile2603/synasc2026/models/gemma-4-31b-it") model = PeftModel.from_pretrained(base_model, "DS4AI-UPB/gemma4-ro-e2e-lora") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -61,7 +61,7 @@ The Romanian training data is machine-translated with automatic post-validation.
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```bibtex
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@misc{vasile2026crosslingual,
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title = {Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian},
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author = {Vasile, Drago\c{s}-Mitru\c{t} and Apostol, Elena-Simona and Toma, \c{S}tefan-Adrian and Truic\u{a}, Ciprian-Octavian},
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year = {2026},
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note = {Preprint}
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}
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```bibtex
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@misc{vasile2026crosslingual,
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title = {Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian},
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author = {Vasile, Drago\c{s}-Mitru\c{t} and Apostol, Elena-Simona and Toma, \c{S}tefan-Adrian and Paschke, Adrian and Truic\u{a}, Ciprian-Octavian},
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year = {2026},
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note = {Preprint}
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}
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