Instructions to use Paladiso/a31e9467-1b84-42f8-a0e6-2c16bbde2ff7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Paladiso/a31e9467-1b84-42f8-a0e6-2c16bbde2ff7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "Paladiso/a31e9467-1b84-42f8-a0e6-2c16bbde2ff7") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from Paladiso/a31e9467-1b84-42f8-a0e6-2c16bbde2ff7: direct link, hf CLI and curl.
- Browser
- Download file 6.9 kB
-
https://huggingface.co/Paladiso/a31e9467-1b84-42f8-a0e6-2c16bbde2ff7/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://Paladiso/a31e9467-1b84-42f8-a0e6-2c16bbde2ff7/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Paladiso/a31e9467-1b84-42f8-a0e6-2c16bbde2ff7/resolve/main/last-checkpoint/training_args.bin
6.9 kB
- Xet hash:
- 547cd048f3bb3ae8daf3f2680abc6dc65aa078ccd6bf2c46e4d9ed5ce8fc6576
- Size of remote file:
- 6.9 kB
- SHA256:
- c19a89f9f5f2f8ac2197e6fbebc8f7b321b3a3656fd63b0f5217d7583ddbb4bf
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