Instructions to use nbninh/3ad0d303-d4a8-4612-a4fe-2b3c3938bb5d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nbninh/3ad0d303-d4a8-4612-a4fe-2b3c3938bb5d with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.4") model = PeftModel.from_pretrained(base_model, "nbninh/3ad0d303-d4a8-4612-a4fe-2b3c3938bb5d") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1bff0600714a427c243e97c98389eb71e0d5b5412636a3f496d117451125264e
- Size of remote file:
- 84 MB
- SHA256:
- 30ab661e139c64e6128b7d83c89f4127e2c5a550038d976130a7b18e9f7e832c
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