Instructions to use trangtrannnnn/6c77dbc4-d1bc-4a31-ac7f-273dbfb39f80 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use trangtrannnnn/6c77dbc4-d1bc-4a31-ac7f-273dbfb39f80 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.5") model = PeftModel.from_pretrained(base_model, "trangtrannnnn/6c77dbc4-d1bc-4a31-ac7f-273dbfb39f80") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c568899e78b2d8315ed899b4bb5ca284bc1eac2e056b7fdb4f97e62fd9ce9c2
- Size of remote file:
- 80.1 MB
- SHA256:
- 7f643e2d4dcae7f9bd5604b2cc1b49d9c56138dc6b761a7bd2073c7a969efdee
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