Instructions to use shibajustfor/b93c8795-5087-436c-95c0-419f21c38c96 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/b93c8795-5087-436c-95c0-419f21c38c96 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, "shibajustfor/b93c8795-5087-436c-95c0-419f21c38c96") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from shibajustfor/b93c8795-5087-436c-95c0-419f21c38c96: direct link, hf CLI and curl.
- Browser
- Download file 55.5 MB
-
https://huggingface.co/shibajustfor/b93c8795-5087-436c-95c0-419f21c38c96/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://shibajustfor/b93c8795-5087-436c-95c0-419f21c38c96/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/shibajustfor/b93c8795-5087-436c-95c0-419f21c38c96/resolve/main/last-checkpoint/optimizer.pt
55.5 MB
- Xet hash:
- e30bd67aa9c528db663e15225113dd1c62e1482771492a73145a58a1d7bc0a52
- Size of remote file:
- 55.5 MB
- SHA256:
- ba4a9547c30761f6436e07261b4fc47ea8108ddf21ff7b0cbe23780509c62cb4
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