Instructions to use nbninh/efa9dfd4-2d39-4c68-9ed8-ef9a3b3e45ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nbninh/efa9dfd4-2d39-4c68-9ed8-ef9a3b3e45ca with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "nbninh/efa9dfd4-2d39-4c68-9ed8-ef9a3b3e45ca") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nbninh/efa9dfd4-2d39-4c68-9ed8-ef9a3b3e45ca: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/nbninh/efa9dfd4-2d39-4c68-9ed8-ef9a3b3e45ca/resolve/main/training_args.bin
- Command line
-
hf download hf://nbninh/efa9dfd4-2d39-4c68-9ed8-ef9a3b3e45ca/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nbninh/efa9dfd4-2d39-4c68-9ed8-ef9a3b3e45ca/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 1c29ece2c0756fdf0753cdb210668092978be4dd665bbac9b851afe989e58ec5
- Size of remote file:
- 6.78 kB
- SHA256:
- bbb05e661a3dd35123f0f558645ac94a8b084f9dbc9967db8f33467b7652db05
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