Instructions to use fats-fme/befa1a68-b759-41cd-aa37-79f4aaa9a6a5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fats-fme/befa1a68-b759-41cd-aa37-79f4aaa9a6a5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B") model = PeftModel.from_pretrained(base_model, "fats-fme/befa1a68-b759-41cd-aa37-79f4aaa9a6a5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66e64b1eaab84be03197025d4e5396422f83b760e660158724dc8dc02aabac34
- Size of remote file:
- 168 MB
- SHA256:
- 6b01fbc615ed658a41d8e3f206bf85fa157be0e474beb28fadc873a4b0f2659a
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