Instructions to use cilooor/08d69553-cdcd-450a-bad2-a81b3c3c8fe7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cilooor/08d69553-cdcd-450a-bad2-a81b3c3c8fe7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "cilooor/08d69553-cdcd-450a-bad2-a81b3c3c8fe7") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cilooor/08d69553-cdcd-450a-bad2-a81b3c3c8fe7: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cilooor/08d69553-cdcd-450a-bad2-a81b3c3c8fe7/resolve/main/training_args.bin
- Command line
-
hf download hf://cilooor/08d69553-cdcd-450a-bad2-a81b3c3c8fe7/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cilooor/08d69553-cdcd-450a-bad2-a81b3c3c8fe7/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 27800dd1ac6c5e73083a4f78dca7f135c40ab8406dc1ed9611446a2e7250372a
- Size of remote file:
- 6.84 kB
- SHA256:
- 44688ca76c617aa4a0e7b2abcf1fb9406dc78caf215a99af7aaa6a0d0b4d29b8
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