Instructions to use eeeebbb2/267607c4-3f58-4ff8-ad72-625352a06ab8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/267607c4-3f58-4ff8-ad72-625352a06ab8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "eeeebbb2/267607c4-3f58-4ff8-ad72-625352a06ab8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d96684823307bd36043303217c1a71afac6ce1c7d3d9775c0c77ea7ebd536d8
- Size of remote file:
- 166 MB
- SHA256:
- 9093e9cf1509a4926eb1b1b7c0f3b1519e350aa4f7e84e4d0080cdfea090819a
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