Instructions to use cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Hermes-3-Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4: direct link, hf CLI and curl.
- Browser
- Download file 43.1 MB
-
https://huggingface.co/cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4/resolve/bdbce323e3f919abfa159267697a018a22b7a1dc/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4@bdbce323e3f919abfa159267697a018a22b7a1dc/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4/resolve/bdbce323e3f919abfa159267697a018a22b7a1dc/last-checkpoint/optimizer.pt
43.1 MB
- Xet hash:
- 59afd5118d795ed9325f134bfea0b3ee319e46ebe98a30d7dede25469e3e216e
- Size of remote file:
- 43.1 MB
- SHA256:
- e07f600fb597088a902196bf21c0d0d7fba2f5afa28d2b50b63125f1fa85e4de
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