Instructions to use aleegis12/e6e6600b-1a23-4227-8804-c69cfc63ce07 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aleegis12/e6e6600b-1a23-4227-8804-c69cfc63ce07 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "aleegis12/e6e6600b-1a23-4227-8804-c69cfc63ce07") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from aleegis12/e6e6600b-1a23-4227-8804-c69cfc63ce07: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/aleegis12/e6e6600b-1a23-4227-8804-c69cfc63ce07/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://aleegis12/e6e6600b-1a23-4227-8804-c69cfc63ce07/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aleegis12/e6e6600b-1a23-4227-8804-c69cfc63ce07/resolve/main/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- 3b4b79cfa232d70a437de7c9ee911d55c44fbbb3e375274fb740a8402b527cfb
- Size of remote file:
- 6.84 kB
- SHA256:
- 18aba7bee0f23c1d841bdd9a9205a9aaa8b0a4ad72d08954865b9bcd59d43b84
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