Instructions to use cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Phi-3-mini-4k-instruct") model = PeftModel.from_pretrained(base_model, "cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7: direct link, hf CLI and curl.
- Browser
- Download file 478 MB
-
https://huggingface.co/cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7/resolve/main/adapter_model.bin
- Command line
-
hf download hf://cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7/resolve/main/adapter_model.bin
478 MB
- Xet hash:
- d0750a4a562d44ac99efb6c87a405a558b835fc475dedb5656294aaae8e33d8b
- Size of remote file:
- 478 MB
- SHA256:
- 8c57814713a695dee2a3d910a74d90b4a81c957f070c3170c2a738c91bae06af
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