Instructions to use julycodes/final-gemma-notes-assessment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use julycodes/final-gemma-notes-assessment with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it") model = PeftModel.from_pretrained(base_model, "julycodes/final-gemma-notes-assessment") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from julycodes/final-gemma-notes-assessment: direct link, hf CLI and curl.
- Browser
- Download file 401 MB
-
https://huggingface.co/julycodes/final-gemma-notes-assessment/resolve/main/optimizer.pt
- Command line
-
hf download hf://julycodes/final-gemma-notes-assessment/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/julycodes/final-gemma-notes-assessment/resolve/main/optimizer.pt
401 MB
- Xet hash:
- bbfbc04b2c2d281a314fb6628a4548e89354edd5bae01ce0de5058d5d3d37187
- Size of remote file:
- 401 MB
- SHA256:
- 6aa3567550884ff75fbdda07cca17a1fa26c34c0c9ecc2d559b18de813922565
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