Instructions to use lhong4759/4b829bbc-a40e-41e4-baef-fa38b03b8069 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lhong4759/4b829bbc-a40e-41e4-baef-fa38b03b8069 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0") model = PeftModel.from_pretrained(base_model, "lhong4759/4b829bbc-a40e-41e4-baef-fa38b03b8069") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2251c6bff2c6c00b2fb78308145dd8b18dab1926ac9430862919148bd324ccf
- Size of remote file:
- 17.2 MB
- SHA256:
- 2539802b4016767e5475c0fa774895f4c33683f7c2ed9df643a61279e9ef1bd2
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