Instructions to use dimasik87/b2cb65a8-cef7-4a04-8365-2e4f31288630 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik87/b2cb65a8-cef7-4a04-8365-2e4f31288630 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, "dimasik87/b2cb65a8-cef7-4a04-8365-2e4f31288630") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from dimasik87/b2cb65a8-cef7-4a04-8365-2e4f31288630: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/dimasik87/b2cb65a8-cef7-4a04-8365-2e4f31288630/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://dimasik87/b2cb65a8-cef7-4a04-8365-2e4f31288630/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dimasik87/b2cb65a8-cef7-4a04-8365-2e4f31288630/resolve/main/last-checkpoint/optimizer.pt
433 MB
- Xet hash:
- 564ae1764841883545ca760716ca1ce2c7b366b9247e508282d9fb13c2184de8
- Size of remote file:
- 433 MB
- SHA256:
- 1ea2790d347072dde0686c590eb5cb4c21b24486ad4823825ab97265b127958e
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