Instructions to use nblinh63/5d1c59d3-b4f3-4d6a-844b-fc69d054f0b1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/5d1c59d3-b4f3-4d6a-844b-fc69d054f0b1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "nblinh63/5d1c59d3-b4f3-4d6a-844b-fc69d054f0b1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b47d89e99511137cd312340ea8cd5556539e8a6a87dc8b627fcd8c42e6ff76b1
- Size of remote file:
- 6.78 kB
- SHA256:
- 83c7f43aa2762adcd2b36b0cd72fb82dd18f5360a0c2cfd256d8d2b72c2c3082
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