Instructions to use gavrilstep/05244df1-90d0-49a7-b329-3ea81d88af20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use gavrilstep/05244df1-90d0-49a7-b329-3ea81d88af20 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B") model = PeftModel.from_pretrained(base_model, "gavrilstep/05244df1-90d0-49a7-b329-3ea81d88af20") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gavrilstep/05244df1-90d0-49a7-b329-3ea81d88af20: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/gavrilstep/05244df1-90d0-49a7-b329-3ea81d88af20/resolve/main/training_args.bin
- Command line
-
hf download hf://gavrilstep/05244df1-90d0-49a7-b329-3ea81d88af20/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gavrilstep/05244df1-90d0-49a7-b329-3ea81d88af20/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- ac3f022eda0be80f623981f0c821189ecec3d56c4fcf4e615806872dafe21051
- Size of remote file:
- 6.78 kB
- SHA256:
- 351673cc0a08f73d98a3b76cdbceeafac30ded7fc0ede0d76da602215881cd03
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