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:
- c64f50bb61da43eadf010e3b200a2f9b12a021954ffe978bae5ff7f8981012c4
- Size of remote file:
- 4.03 MB
- SHA256:
- 7c857c4f1a9a1acdb7c52ba1e0a23d267e8282065a30872da0a78a650d517092
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