Instructions to use ancient41/237b4e62-58ce-41e3-86eb-6bec254babad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ancient41/237b4e62-58ce-41e3-86eb-6bec254babad with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.1-Storm-8B") model = PeftModel.from_pretrained(base_model, "ancient41/237b4e62-58ce-41e3-86eb-6bec254babad") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ancient41/237b4e62-58ce-41e3-86eb-6bec254babad: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/ancient41/237b4e62-58ce-41e3-86eb-6bec254babad/resolve/main/training_args.bin
- Command line
-
hf download hf://ancient41/237b4e62-58ce-41e3-86eb-6bec254babad/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ancient41/237b4e62-58ce-41e3-86eb-6bec254babad/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 937d73d1855cdb64913c9fec8ef60ee69a41114acb6d2ec12d2986331a98167c
- Size of remote file:
- 6.84 kB
- SHA256:
- 74fb3703d7cb61f22a9770777f4b94d01a11071b9961aeae1d08ca740b9af8de
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