Instructions to use dada22231/f0e02592-fed1-457a-aad1-7cd8fbbef9f4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dada22231/f0e02592-fed1-457a-aad1-7cd8fbbef9f4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceM4/tiny-random-LlamaForCausalLM") model = PeftModel.from_pretrained(base_model, "dada22231/f0e02592-fed1-457a-aad1-7cd8fbbef9f4") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from dada22231/f0e02592-fed1-457a-aad1-7cd8fbbef9f4: direct link, hf CLI and curl.
- Browser
- Download file 104 kB
-
https://huggingface.co/dada22231/f0e02592-fed1-457a-aad1-7cd8fbbef9f4/resolve/main/adapter_model.bin
- Command line
-
hf download hf://dada22231/f0e02592-fed1-457a-aad1-7cd8fbbef9f4/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/dada22231/f0e02592-fed1-457a-aad1-7cd8fbbef9f4/resolve/main/adapter_model.bin
104 kB
- Xet hash:
- e5cfd178fde4007203b996be6831b1fedd237d2f05a416bfa5047634db209501
- Size of remote file:
- 104 kB
- SHA256:
- 9f7ca012c20e5d3b4bee5af7a23be771b2d24e1f449473df490164fde0c7c12d
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