Instructions to use Aivesa/123065a1-9b3c-4f1d-9da6-851c05141cb8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Aivesa/123065a1-9b3c-4f1d-9da6-851c05141cb8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TitanML/tiny-mixtral") model = PeftModel.from_pretrained(base_model, "Aivesa/123065a1-9b3c-4f1d-9da6-851c05141cb8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff999fda3ce40a6998b96354a3c8962edce16bf9f9fdf4a4ac812bff545d46dc
- Size of remote file:
- 6.9 kB
- SHA256:
- ee96e920067c487a15983c445f6c2fa74ca7bac0a485b6c8be81e928f35cf904
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