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 training_args.bin from aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433/resolve/main/training_args.bin
- Command line
-
hf download hf://aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 2622e57dcb30fec6334e2f407556ee313153d5eb4a2eb6f9f6f3f2bf9e3deca9
- Size of remote file:
- 6.84 kB
- SHA256:
- 758e0c6348870d2d408a8280573e246cd947b47e34adaeec937ace24d10e5c1a
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