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 training_args.bin from shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2/resolve/main/training_args.bin
- Command line
-
hf download hf://shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 7b1ad25350e6e16ea1e1e0b8242f501d884c2d70969ad8fdb70151f5e7d72380
- Size of remote file:
- 6.78 kB
- SHA256:
- 2bb8bd5aeb9ec22b3e2fe8658727bd5e8e809d6bd52d7222d21a0a89bd128c7d
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