Instructions to use Serega6678/prototype_joint_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Serega6678/prototype_joint_trained with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "Serega6678/prototype_joint_trained") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e08271b9e7978b9ea73b0c5e2d6ee00d90eed651035302153d86caa492a9071
- Size of remote file:
- 4.86 kB
- SHA256:
- 83b954d34248394117f3285ae60cb26b5583f52dba1d2f1601296096333fc136
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