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 tokenizer.json from julycodes/final-gemma-notes-assessment: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/julycodes/final-gemma-notes-assessment/resolve/main/tokenizer.json
- Command line
-
hf download hf://julycodes/final-gemma-notes-assessment/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/julycodes/final-gemma-notes-assessment/resolve/main/tokenizer.json
17.5 MB
- Xet hash:
- e3d966a7ae8d026d0a9c58605f5a2d7223bb451e7aec3ebde81485ab679d1a6d
- Size of remote file:
- 17.5 MB
- SHA256:
- ce19157ce6b457736a0015a2a4fb06c966c6ff252ec9d1950777eec6598abf6d
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