Instructions to use Paladiso/d72ef992-2955-4289-aef8-fcc6be507dfb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Paladiso/d72ef992-2955-4289-aef8-fcc6be507dfb with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-7b-it") model = PeftModel.from_pretrained(base_model, "Paladiso/d72ef992-2955-4289-aef8-fcc6be507dfb") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from Paladiso/d72ef992-2955-4289-aef8-fcc6be507dfb: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/Paladiso/d72ef992-2955-4289-aef8-fcc6be507dfb/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://Paladiso/d72ef992-2955-4289-aef8-fcc6be507dfb/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Paladiso/d72ef992-2955-4289-aef8-fcc6be507dfb/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- eb7f590125f55bb7b98dbda48fd22e11e033d70ec961f9ecb8bac8b7af57c79f
- Size of remote file:
- 6.78 kB
- SHA256:
- beb2c0ff6fc19c0828b8316dbc6b0eda59725c85c761d275736801fbf30ff105
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