Text Generation
PEFT
Safetensors
Arabic
arabic
relation-extraction
qlora
bitsandbytes
multiple-choice
conversational
Instructions to use U4RASD/DRU-RE-Yehia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use U4RASD/DRU-RE-Yehia with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Navid-AI/Yehia-7B-preview") model = PeftModel.from_pretrained(base_model, "U4RASD/DRU-RE-Yehia") - Notebooks
- Google Colab
- Kaggle
| { | |
| "package": "DRU_RE_Yehia_FullPipeline_Test", | |
| "version": "1.0.0", | |
| "created_for": "RunPod blind test inference", | |
| "input_default": "/workspace/test.jsonl", | |
| "output_default": "/workspace/submission.zip", | |
| "models": { | |
| "type_predictor": "U4RASD/TypePredictor", | |
| "adapter": "U4RASD/DRU-RE-Yehia", | |
| "base": "Navid-AI/Yehia-7B-preview", | |
| "base_revision": "b9dda4715eafee7e8090d2c83cfe078d75f4ebb8" | |
| }, | |
| "primary_aggregation": "majority_vote", | |
| "files": [ | |
| ".env.template", | |
| "README.md", | |
| "requirements.txt", | |
| "run_test.py", | |
| "run_test.sh", | |
| "setup_env.sh", | |
| "type_predictor.py" | |
| ] | |
| } | |