Instructions to use bane5631/b16d3ae9-0778-4227-8847-a52a0826b669 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bane5631/b16d3ae9-0778-4227-8847-a52a0826b669 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B") model = PeftModel.from_pretrained(base_model, "bane5631/b16d3ae9-0778-4227-8847-a52a0826b669") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31bc3aa88b3b7eb80b5ac428c766e49995b81481d1bae72a48a2b0d7b6cbface
- Size of remote file:
- 6.78 kB
- SHA256:
- dcaf141dca9155d222beca9de7a02d42e273a267be42b536ad5d049648d6fa74
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