Instructions to use aleegis12/3b730adc-d470-4762-b06e-90958b76b363 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aleegis12/3b730adc-d470-4762-b06e-90958b76b363 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, "aleegis12/3b730adc-d470-4762-b06e-90958b76b363") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aleegis12/3b730adc-d470-4762-b06e-90958b76b363: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/aleegis12/3b730adc-d470-4762-b06e-90958b76b363/resolve/main/training_args.bin
- Command line
-
hf download hf://aleegis12/3b730adc-d470-4762-b06e-90958b76b363/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aleegis12/3b730adc-d470-4762-b06e-90958b76b363/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- ac4d25947d9962aaca141beb9918770fe01e503cc748654b1b6740d5515d7d95
- Size of remote file:
- 6.84 kB
- SHA256:
- 14c7d8b7330f2c5867b8a64df02f530d09f45c26b103629ede30c5f33b399553
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