Instructions to use shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5") model = PeftModel.from_pretrained(base_model, "shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69: direct link, hf CLI and curl.
- Browser
- Download file 14.7 MB
-
https://huggingface.co/shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69/resolve/main/last-checkpoint/optimizer.pt
14.7 MB
- Xet hash:
- 7e09eee2e097bddcc9328a3b4939c2fc0ae50c575e8422b6e6293ec2f84bdb6a
- Size of remote file:
- 14.7 MB
- SHA256:
- 70ee5ef0f5768aa9bd93c29d10722f458c38da44a31ec8f1974bfeb4913905dd
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