Instructions to use thangla01/70e31d4e-2c6c-4d94-a9c6-56a0af123b68 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use thangla01/70e31d4e-2c6c-4d94-a9c6-56a0af123b68 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, "thangla01/70e31d4e-2c6c-4d94-a9c6-56a0af123b68") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2082f38aa60ce9851e59bde85f9c20905c6bbd0c051faf64b5cb82c9937cd0d9
- Size of remote file:
- 608 MB
- SHA256:
- fe4c0651c581ce4225e31304a4805ed01cd49c55ff06c16eeada4b01a22cc9e9
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