Instructions to use dimasik2987/4bdd484a-3e63-4ba5-b86a-5dca922ef9e2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik2987/4bdd484a-3e63-4ba5-b86a-5dca922ef9e2 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, "dimasik2987/4bdd484a-3e63-4ba5-b86a-5dca922ef9e2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a782ad667ef7b898bcf5d0df01f860eefee0c061775d2dc3d6743d0ced98bfd
- Size of remote file:
- 336 MB
- SHA256:
- 7a8a35651c1e0f486544d82912d9abd79f21b1d8e3e3a31b0a54ca9430b9f8a9
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