Instructions to use kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Hermes-3-Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905/resolve/main/training_args.bin
- Command line
-
hf download hf://kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 3c64a628a5d27aa7b4988a603de8b8675d1832e5be56ef0d34f543d3a6394b4b
- Size of remote file:
- 6.78 kB
- SHA256:
- be540bba788b74a33fc64aac02640bce0c1314e4611044c245f7b0423b4347b4
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