Instructions to use kk-aivio/83406873-a003-4e0b-a417-07237f916c48 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kk-aivio/83406873-a003-4e0b-a417-07237f916c48 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("peft-internal-testing/tiny-dummy-qwen2") model = PeftModel.from_pretrained(base_model, "kk-aivio/83406873-a003-4e0b-a417-07237f916c48") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from kk-aivio/83406873-a003-4e0b-a417-07237f916c48: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/kk-aivio/83406873-a003-4e0b-a417-07237f916c48/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://kk-aivio/83406873-a003-4e0b-a417-07237f916c48/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kk-aivio/83406873-a003-4e0b-a417-07237f916c48/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- 19b47c43a84192d4724243434bd3e3ced641c23ef0b96ae4df9c13c13acb12e1
- Size of remote file:
- 6.78 kB
- SHA256:
- 07771d64bca2f4b967d739e719a90b6dbf3b1f53719b1ab41562f14cef075f22
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