Instructions to use shibajustfor/f90cf39e-f29a-4347-8bfd-2be08766d27f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/f90cf39e-f29a-4347-8bfd-2be08766d27f with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/input_data/jingyeom/seal3.1.6n_7b") model = PeftModel.from_pretrained(base_model, "shibajustfor/f90cf39e-f29a-4347-8bfd-2be08766d27f") - Notebooks
- Google Colab
- Kaggle
shibajustfor/f90cf39e-f29a-4347-8bfd-2be08766d27f
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7508
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
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Model tree for shibajustfor/f90cf39e-f29a-4347-8bfd-2be08766d27f
Base model
jingyeom/seal3.1.6n_7b