Instructions to use gavrilstep/9da99aa7-a7c0-4c89-95e4-45d78b1ce056 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use gavrilstep/9da99aa7-a7c0-4c89-95e4-45d78b1ce056 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-13b-v1.5") model = PeftModel.from_pretrained(base_model, "gavrilstep/9da99aa7-a7c0-4c89-95e4-45d78b1ce056") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 157f409006cfc602458a3c1b196cf8eaec10308d17c93692cfab62eed293603e
- Size of remote file:
- 251 MB
- SHA256:
- 5cd213ec5336a64c570c9aa05e80d9ed94a6df5dc10622350fb930ae3bd19201
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