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:
- bdd1d1a5b67d0749fffeb823ca84a08401df2b45f81a62c0e28e9d5bc520be72
- Size of remote file:
- 104 kB
- SHA256:
- 7ea9e3214cf784cea5fd1cb722670b61389aaee3fd5677b5f8eddf3cca220419
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