Instructions to use dzanbek/b19f0afb-87c9-4c3a-9b22-183983e96095 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dzanbek/b19f0afb-87c9-4c3a-9b22-183983e96095 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, "dzanbek/b19f0afb-87c9-4c3a-9b22-183983e96095") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from dzanbek/b19f0afb-87c9-4c3a-9b22-183983e96095: direct link, hf CLI and curl.
- Browser
- Download file 23.3 kB
-
https://huggingface.co/dzanbek/b19f0afb-87c9-4c3a-9b22-183983e96095/resolve/main/adapter_model.bin
- Command line
-
hf download hf://dzanbek/b19f0afb-87c9-4c3a-9b22-183983e96095/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/dzanbek/b19f0afb-87c9-4c3a-9b22-183983e96095/resolve/main/adapter_model.bin
23.3 kB
- Xet hash:
- 6c104786e24b3e540396a29271f4818c7634b851043e33e13209c7ac932906f0
- Size of remote file:
- 23.3 kB
- SHA256:
- aaeba2b0a64e51af1c96e46fe9577f99eb04e703adad43c5ec896b021a5d88b7
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