Instructions to use nblinh/026b8401-b8fe-4d28-894b-dff3380bff97 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/026b8401-b8fe-4d28-894b-dff3380bff97 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("heegyu/WizardVicuna-open-llama-3b-v2") model = PeftModel.from_pretrained(base_model, "nblinh/026b8401-b8fe-4d28-894b-dff3380bff97") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f865242136ec33fa84b483e51c3268f16a2a43e73cc1c64f88f81865d7d2de1a
- Size of remote file:
- 102 MB
- SHA256:
- a6f2e1f1bd0b6abc9587002d4df72053e87a2471d286a6ddb9bdf9c83d0f241e
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