Instructions to use adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Capybara-7B-V1") model = PeftModel.from_pretrained(base_model, "adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8: direct link, hf CLI and curl.
- Browser
- Download file 604 MB
-
https://huggingface.co/adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8/resolve/main/adapter_model.bin
- Command line
-
hf download hf://adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8/resolve/main/adapter_model.bin
604 MB
- Xet hash:
- 42d6614368c68be4845ab8e9214d085933f9ad99d60188cf5c30dedd01e26b4e
- Size of remote file:
- 604 MB
- SHA256:
- 417a0389971d4dace3d0c9350e2d0202826e5f39371bd3bcb2179d14b393f440
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