Instructions to use eeeebbb2/f0e02592-fed1-457a-aad1-7cd8fbbef9f4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/f0e02592-fed1-457a-aad1-7cd8fbbef9f4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceM4/tiny-random-LlamaForCausalLM") model = PeftModel.from_pretrained(base_model, "eeeebbb2/f0e02592-fed1-457a-aad1-7cd8fbbef9f4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e8abfa20bbe5dbe86e07fdf8b711eb6aac8f6241af63cea60261e27489678f9
- Size of remote file:
- 6.84 kB
- SHA256:
- 1939555b7debc5fae5de297beb5fac93789efd1298ba39304425ea1b5cbb926a
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