Instructions to use phungkhaccuong/32d80887-0374-43a4-bac6-176065c15fda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use phungkhaccuong/32d80887-0374-43a4-bac6-176065c15fda with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4") model = PeftModel.from_pretrained(base_model, "phungkhaccuong/32d80887-0374-43a4-bac6-176065c15fda") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0db4baf791d30b385cdb00944b44e26263302bcdfdf3ec28f995824d69158ca
- Size of remote file:
- 608 MB
- SHA256:
- b4c10a6a70998a857959b01806a06be8faf1a1c5cff44040b5f0e65778650c03
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