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 training_args.bin from julycodes/final-gemma-notes-assessment: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/julycodes/final-gemma-notes-assessment/resolve/main/training_args.bin
- Command line
-
hf download hf://julycodes/final-gemma-notes-assessment/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/julycodes/final-gemma-notes-assessment/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 9e62e4a0902afa2a53a0036ac7079509dc04082469ba857a949ce7ccfe20cfad
- Size of remote file:
- 4.92 kB
- SHA256:
- c6776507015d5062729966e7dab24eb3614937306444c1dbbd46f2d1a0333736
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