Instructions to use dimasik87/d92da16d-68e1-4598-a7e3-378a0232bf0a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik87/d92da16d-68e1-4598-a7e3-378a0232bf0a with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2") model = PeftModel.from_pretrained(base_model, "dimasik87/d92da16d-68e1-4598-a7e3-378a0232bf0a") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from dimasik87/d92da16d-68e1-4598-a7e3-378a0232bf0a: direct link, hf CLI and curl.
- Browser
- Download file 216 MB
-
https://huggingface.co/dimasik87/d92da16d-68e1-4598-a7e3-378a0232bf0a/resolve/main/adapter_model.bin
- Command line
-
hf download hf://dimasik87/d92da16d-68e1-4598-a7e3-378a0232bf0a/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/dimasik87/d92da16d-68e1-4598-a7e3-378a0232bf0a/resolve/main/adapter_model.bin
216 MB
- Xet hash:
- bb643d554f2b3c389c85305821be15afc928128df4c00500ddff9cef5089f758
- Size of remote file:
- 216 MB
- SHA256:
- c4eb2f009c996cf20840c57f3b3fee172edbe87d6ed4134e66152fc3ac1f6cae
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