Instructions to use vmpsergio/380acb2c-500a-4b09-b01c-c87808f2853a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vmpsergio/380acb2c-500a-4b09-b01c-c87808f2853a with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-llama-2-7b") model = PeftModel.from_pretrained(base_model, "vmpsergio/380acb2c-500a-4b09-b01c-c87808f2853a") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb750941cffaa9329377bbb183f5fd4e9c17e7dee0387482223336e1c2c4c9a6
- Size of remote file:
- 844 MB
- SHA256:
- 3c7dba9667cc21c171d8e73ffb7e9471a8a3499ab1fc6325bee66ce2ebc83e1e
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