Instructions to use adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("fxmarty/tiny-random-GemmaForCausalLM") model = PeftModel.from_pretrained(base_model, "adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7: direct link, hf CLI and curl.
- Browser
- Download file 14.4 kB
-
https://huggingface.co/adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7/resolve/main/adapter_model.bin
- Command line
-
hf download hf://adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7/resolve/main/adapter_model.bin
14.4 kB
- Xet hash:
- f0c89844c427cf88a7fd221e38b406b9d9a11eb2fc7819ad500369c14d2b11a9
- Size of remote file:
- 14.4 kB
- SHA256:
- 2ddb3041c60d7f90f1133d3cc75f1f5aca7bff3f43bbf565c18e1bcb00efcfca
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