Instructions to use eeeebbb2/fd0ee068-8a66-4aa1-9803-9b270d06e76a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/fd0ee068-8a66-4aa1-9803-9b270d06e76a with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Theta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "eeeebbb2/fd0ee068-8a66-4aa1-9803-9b270d06e76a") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ada409da4953912dc6c3654acb52db0c822393d82d9e3e5074c892a2bb571fce
- Size of remote file:
- 2.44 GB
- SHA256:
- 2e85e6ac4af4d86ec00dd4f38cd9d998d8bbd59d232d27f4b9e179f2a1702d12
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