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.safetensors 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.safetensors
- Command line
-
hf download hf://cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7/resolve/main/adapter_model.safetensors
478 MB
- Xet hash:
- 5d3661d008f2c26af4bb5e848fc6ae0d3c6f902f78db9c7167aa5b1d8a65f11b
- Size of remote file:
- 478 MB
- SHA256:
- 97bae6529d98242cb5e72fce8a1b757b7a69ef665fce21bf50e6a6e3fe84267c
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