Instructions to use eeeebbb2/497c96c9-c38c-4b4c-940a-c6540043e3dc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/497c96c9-c38c-4b4c-940a-c6540043e3dc with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.3") model = PeftModel.from_pretrained(base_model, "eeeebbb2/497c96c9-c38c-4b4c-940a-c6540043e3dc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c86e17ed4d964cd1fac8bd3a762583ce94a7a30438d2f29663ae1e0775c5ff09
- Size of remote file:
- 320 MB
- SHA256:
- 73c9f6fa5d5f3baedb06beed8d03d1516b625b91bf236064f139f0b9cad568b9
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