Instructions to use cwaud/b2b03232-2afd-41d4-b17e-131b039c511a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cwaud/b2b03232-2afd-41d4-b17e-131b039c511a 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, "cwaud/b2b03232-2afd-41d4-b17e-131b039c511a") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from cwaud/b2b03232-2afd-41d4-b17e-131b039c511a: direct link, hf CLI and curl.
- Browser
- Download file 10.6 kB
-
https://huggingface.co/cwaud/b2b03232-2afd-41d4-b17e-131b039c511a/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://cwaud/b2b03232-2afd-41d4-b17e-131b039c511a/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/cwaud/b2b03232-2afd-41d4-b17e-131b039c511a/resolve/main/adapter_model.safetensors
10.6 kB
- Xet hash:
- 37893598a9d979520894647f61fa6cea74c046027b3040973d17077fb6963e06
- Size of remote file:
- 10.6 kB
- SHA256:
- b74cf4e09c5ed83bb82d0e7d8e3d6d510d96367ff96a844212b7de14580c5266
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