Instructions to use mamung/2c15f6fc-8832-491b-bb73-ac4775664582 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mamung/2c15f6fc-8832-491b-bb73-ac4775664582 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/tinyllama") model = PeftModel.from_pretrained(base_model, "mamung/2c15f6fc-8832-491b-bb73-ac4775664582") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bbb617e91ba27a8a790dbcfd8e25f75358a11b12ee0a70587addbe0f5994f13
- Size of remote file:
- 6.84 kB
- SHA256:
- 64071923b1c35b1979b34b56953a2032aebf409e4bcb92df31e28464d9b78e82
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