Instructions to use FatCat87/taopanda-3_3fd1ec50-91e1-4e9c-a1cb-46229e41c04c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use FatCat87/taopanda-3_3fd1ec50-91e1-4e9c-a1cb-46229e41c04c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/codellama-7b") model = PeftModel.from_pretrained(base_model, "FatCat87/taopanda-3_3fd1ec50-91e1-4e9c-a1cb-46229e41c04c") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a3f1b72dc39c1a09ed17eef5984abb07cebba8fff23f7a94499e74cf648c942
- Size of remote file:
- 6.2 kB
- SHA256:
- 3d225f1e6efe9b96baf35a08d849833ec68ec6d1e05a94fc418d2d1191107689
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