Instructions to use adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM-135M-Instruct") model = PeftModel.from_pretrained(base_model, "adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032: direct link, hf CLI and curl.
- Browser
- Download file 9.92 MB
-
https://huggingface.co/adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032/resolve/main/adapter_model.bin
- Command line
-
hf download hf://adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032/resolve/main/adapter_model.bin
9.92 MB
- Xet hash:
- 67047dd7da31b3f12094141d7199662fb1c0eb9894f31a64d711700209569891
- Size of remote file:
- 9.92 MB
- SHA256:
- 3543ea28061840626c06ddce02ea54e10675023d4c23b551400d725dd7f4e426
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