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 last-checkpoint/optimizer.pt from aleegis/f2d32988-6039-4631-a6d2-23550fc997a5: direct link, hf CLI and curl.
- Browser
- Download file 671 MB
-
https://huggingface.co/aleegis/f2d32988-6039-4631-a6d2-23550fc997a5/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://aleegis/f2d32988-6039-4631-a6d2-23550fc997a5/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/aleegis/f2d32988-6039-4631-a6d2-23550fc997a5/resolve/main/last-checkpoint/optimizer.pt
671 MB
- Xet hash:
- 7065c2b30f330adf50eedff5d61c202c760f30b4262df0255da2a3cbf7993a96
- Size of remote file:
- 671 MB
- SHA256:
- 02bd66363a55075e209836624ecb54de2195ec9603078f8e8f2b8ef1c42fa3cd
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