Instructions to use minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("dltjdgh0928/test_instruction") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357/resolve/main/training_args.bin
- Command line
-
hf download hf://minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- b014ef7b9e42e2dfe9d0f9aaf076830edf47c3562394ecd6cd311e5c02857fcc
- Size of remote file:
- 6.78 kB
- SHA256:
- 178d71aba3e1539d43fc8a126175f09fb2e08c809d16ebbe69b57163c0c5f715
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