Instructions to use kk-aivio/e32c36e6-0244-4c42-b6a3-c465807eaccb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kk-aivio/e32c36e6-0244-4c42-b6a3-c465807eaccb with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0") model = PeftModel.from_pretrained(base_model, "kk-aivio/e32c36e6-0244-4c42-b6a3-c465807eaccb") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kk-aivio/e32c36e6-0244-4c42-b6a3-c465807eaccb: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/kk-aivio/e32c36e6-0244-4c42-b6a3-c465807eaccb/resolve/main/training_args.bin
- Command line
-
hf download hf://kk-aivio/e32c36e6-0244-4c42-b6a3-c465807eaccb/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kk-aivio/e32c36e6-0244-4c42-b6a3-c465807eaccb/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 3a0475a790a70d5c4b9afb6574b93129aaf3fe4f1774933e1c75ddcd2db992d2
- Size of remote file:
- 6.78 kB
- SHA256:
- e68f662879048443b65558857ffac046c91a0e386eef07d9e57f7e54f1f6d81d
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