Instructions to use aleegis/f2d32988-6039-4631-a6d2-23550fc997a5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aleegis/f2d32988-6039-4631-a6d2-23550fc997a5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B") model = PeftModel.from_pretrained(base_model, "aleegis/f2d32988-6039-4631-a6d2-23550fc997a5") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aleegis/f2d32988-6039-4631-a6d2-23550fc997a5: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/aleegis/f2d32988-6039-4631-a6d2-23550fc997a5/resolve/main/training_args.bin
- Command line
-
hf download hf://aleegis/f2d32988-6039-4631-a6d2-23550fc997a5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aleegis/f2d32988-6039-4631-a6d2-23550fc997a5/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- c2a57be5c2c59ceb09c8737bc31a1cdd4dc5ac1fd4aa837adc096209a08b6f2b
- Size of remote file:
- 6.78 kB
- SHA256:
- 9b03498fea8982d34fbf0722a1a2b68efff7a18814b360323427594035530991
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