Instructions to use cwaud/b2b03232-2afd-41d4-b17e-131b039c511a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cwaud/b2b03232-2afd-41d4-b17e-131b039c511a with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("fxmarty/tiny-random-GemmaForCausalLM") model = PeftModel.from_pretrained(base_model, "cwaud/b2b03232-2afd-41d4-b17e-131b039c511a") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cwaud/b2b03232-2afd-41d4-b17e-131b039c511a: direct link, hf CLI and curl.
- Browser
- Download file 6.71 kB
-
https://huggingface.co/cwaud/b2b03232-2afd-41d4-b17e-131b039c511a/resolve/main/training_args.bin
- Command line
-
hf download hf://cwaud/b2b03232-2afd-41d4-b17e-131b039c511a/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cwaud/b2b03232-2afd-41d4-b17e-131b039c511a/resolve/main/training_args.bin
6.71 kB
- Xet hash:
- d5547f9d8bd6ded957d76ac239ad81e4cf0f74a0945b780eb7cabb0c1d94d105
- Size of remote file:
- 6.71 kB
- SHA256:
- 9177d5937174de1902bbe62bfa0b58b293fcb01a1bf0439cea55ab8c614b93ef
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