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:
- 1bf2b0bfa6483532144c260c6352c3d5adf7b748d524c09159e686e08c171b02
- Size of remote file:
- 168 MB
- SHA256:
- e25c6ffb12c326452118842e8df90a7c3a30f12879618861387152fa89f97872
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