Instructions to use adammandic87/e8a39720-816d-43c5-a9bf-d672eb225743 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/e8a39720-816d-43c5-a9bf-d672eb225743 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta") model = PeftModel.from_pretrained(base_model, "adammandic87/e8a39720-816d-43c5-a9bf-d672eb225743") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8968c2a1d1224d11dfec3a9b76c5535d869d1f5df21207403e835f216ac8829
- Size of remote file:
- 83.9 MB
- SHA256:
- 1866e7c7b74d9ff773ec32c380e70c6692fa7b656730b02f0af83068200658d5
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