Instructions to use shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/tiny-random-LlamaForCausalLM") model = PeftModel.from_pretrained(base_model, "shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2: direct link, hf CLI and curl.
- Browser
- Download file 33.7 kB
-
https://huggingface.co/shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2/resolve/main/adapter_model.bin
- Command line
-
hf download hf://shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2/resolve/main/adapter_model.bin
33.7 kB
- Xet hash:
- 7c6a85fffd6c5b04816240cd011f4570423d5f88632247f9ead3f3bdf96881d7
- Size of remote file:
- 33.7 kB
- SHA256:
- a368e5c2486c9dbf324bf4e3d95e35c5b8c40c40baa829031e446c6813419077
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