Instructions to use daniel40/47f8eeca-3c8b-4585-bcf6-4fd378fb2001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use daniel40/47f8eeca-3c8b-4585-bcf6-4fd378fb2001 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2") model = PeftModel.from_pretrained(base_model, "daniel40/47f8eeca-3c8b-4585-bcf6-4fd378fb2001") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from daniel40/47f8eeca-3c8b-4585-bcf6-4fd378fb2001: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/daniel40/47f8eeca-3c8b-4585-bcf6-4fd378fb2001/resolve/main/training_args.bin
- Command line
-
hf download hf://daniel40/47f8eeca-3c8b-4585-bcf6-4fd378fb2001/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/daniel40/47f8eeca-3c8b-4585-bcf6-4fd378fb2001/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 4da8c39c4dca17633641dd56727b54174bdcb489f363e01d315cf8f3d167f8c8
- Size of remote file:
- 6.78 kB
- SHA256:
- 441ef3c539af30730ba0cb88cd3766f03efbb9b38b7c518bdebae60cf5cc8b93
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