Instructions to use cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-1.1-2b-it") model = PeftModel.from_pretrained(base_model, "cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628: direct link, hf CLI and curl.
- Browser
- Download file 314 MB
-
https://huggingface.co/cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628/resolve/main/adapter_model.bin
- Command line
-
hf download hf://cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/cilorku/2e695c5f-bec4-41a1-b82e-463bf3aac628/resolve/main/adapter_model.bin
314 MB
- Xet hash:
- 1b477e2e0be876f77e04b539ab92fd3abb9ea4641324b7198155d8f155fdd551
- Size of remote file:
- 314 MB
- SHA256:
- 08c91d3cae524b061714b2e204ab0219862080ecfdd8044a6c63c689023fa894
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