Instructions to use aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("echarlaix/tiny-random-mistral") model = PeftModel.from_pretrained(base_model, "aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433: direct link, hf CLI and curl.
- Browser
- Download file 231 kB
-
https://huggingface.co/aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433/resolve/main/adapter_model.bin
- Command line
-
hf download hf://aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433/resolve/main/adapter_model.bin
231 kB
- Xet hash:
- e2493c718eb84dcde23e8643556d4cadd2f2649f1169c09934e862178c2e6adf
- Size of remote file:
- 231 kB
- SHA256:
- e3c5b7af8d8446281b51cc4cb5beeca17cdb3ad3e12c8c8c48cc21dfdf928e7e
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