Instructions to use cunghoctienganh/4c94280d-b161-4759-bcab-c9101b914dd5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cunghoctienganh/4c94280d-b161-4759-bcab-c9101b914dd5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("rayonlabs/e3f77680-ac2a-4c6f-afed-0b2386f29ee7") model = PeftModel.from_pretrained(base_model, "cunghoctienganh/4c94280d-b161-4759-bcab-c9101b914dd5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7126387dc7c8a106cd2b2dd1dbee35770acd344f866913dda212451d300ea43d
- Size of remote file:
- 84 MB
- SHA256:
- 50e089975d72d4716973d83611bace3118bc416b3d1461ba59803d71eac96d27
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