Instructions to use cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-1.1-2b-it") model = PeftModel.from_pretrained(base_model, "cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628/resolve/main/training_args.bin
- Command line
-
hf download hf://cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 498efe068b52908f84b27c88172704e1713bcea88322a7a96eecc32ea3a03184
- Size of remote file:
- 6.84 kB
- SHA256:
- f62181c0cb8da0437a3879c2921f66dd71ee08fea589fb3fdb51bb7b2d9759a0
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