Instructions to use bane5631/9c0c28c0-b9de-445c-9048-dc3c608ae5e6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bane5631/9c0c28c0-b9de-445c-9048-dc3c608ae5e6 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-135M") model = PeftModel.from_pretrained(base_model, "bane5631/9c0c28c0-b9de-445c-9048-dc3c608ae5e6") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from bane5631/9c0c28c0-b9de-445c-9048-dc3c608ae5e6: direct link, hf CLI and curl.
- Browser
- Download file 9.92 MB
-
https://huggingface.co/bane5631/9c0c28c0-b9de-445c-9048-dc3c608ae5e6/resolve/main/adapter_model.bin
- Command line
-
hf download hf://bane5631/9c0c28c0-b9de-445c-9048-dc3c608ae5e6/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/bane5631/9c0c28c0-b9de-445c-9048-dc3c608ae5e6/resolve/main/adapter_model.bin
9.92 MB
- Xet hash:
- d571ebfee8051990e1f6d91a1dcf3fee5bd8391a50cf815bd1207ac57f495503
- Size of remote file:
- 9.92 MB
- SHA256:
- 591c98fe7352969eff06bb75f45b351c399b481fba58c505969616c397b14854
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