Instructions to use cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4 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, "cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4: direct link, hf CLI and curl.
- Browser
- Download file 6.71 kB
-
https://huggingface.co/cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4/resolve/0ec811b92c8349d523a48d243e14ee4fe5cd6c7f/training_args.bin
- Command line
-
hf download hf://cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4@0ec811b92c8349d523a48d243e14ee4fe5cd6c7f/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cwaud/29a9a50b-b225-4aac-afd7-8d1228fe1ff4/resolve/0ec811b92c8349d523a48d243e14ee4fe5cd6c7f/training_args.bin
6.71 kB
- Xet hash:
- 5e9a129aa426e98443fa3551fee2adc1b81f851343b1823966ca7adc2bd3167a
- Size of remote file:
- 6.71 kB
- SHA256:
- 3cd151660018b691f4494b5d43b1d8aa9a78460158a27000a6d2980f75ca8cbd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.