Instructions to use dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM-135M-Instruct") model = PeftModel.from_pretrained(base_model, "dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9/resolve/main/training_args.bin
- Command line
-
hf download hf://dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 171411a40bec858f03f821c9d5169cfdf3f1ef03dcbd64fcd732f94e735ad134
- Size of remote file:
- 6.84 kB
- SHA256:
- 57fc026f0ec22a1ff461f6042803950ad5ef3c329ff5e48df09e1a585d358652
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