Instructions to use gavrilstep/85aa9e5d-cbb6-4fe6-9e71-d69f333295b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use gavrilstep/85aa9e5d-cbb6-4fe6-9e71-d69f333295b0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Theta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "gavrilstep/85aa9e5d-cbb6-4fe6-9e71-d69f333295b0") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gavrilstep/85aa9e5d-cbb6-4fe6-9e71-d69f333295b0: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/gavrilstep/85aa9e5d-cbb6-4fe6-9e71-d69f333295b0/resolve/df865f8bf91b3e69e0eaf5aed289974162502cb4/training_args.bin
- Command line
-
hf download hf://gavrilstep/85aa9e5d-cbb6-4fe6-9e71-d69f333295b0@df865f8bf91b3e69e0eaf5aed289974162502cb4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gavrilstep/85aa9e5d-cbb6-4fe6-9e71-d69f333295b0/resolve/df865f8bf91b3e69e0eaf5aed289974162502cb4/training_args.bin
6.78 kB
- Xet hash:
- 0034309e188087d67ed9fe67060025b4f5c8c282b33d763c5481e106e554efed
- Size of remote file:
- 6.78 kB
- SHA256:
- 51cf1b907f30e0efab372eaca13f7b08a8227aab4ea63371c891141e192e47e5
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