Instructions to use FatCat87/taopanda-1_2a7c9c92-3917-4572-945e-c424180804e1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use FatCat87/taopanda-1_2a7c9c92-3917-4572-945e-c424180804e1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-9b") model = PeftModel.from_pretrained(base_model, "FatCat87/taopanda-1_2a7c9c92-3917-4572-945e-c424180804e1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 119c1d42c8103a59996b21853053cd3c4019bf20d58acac228cc6e8ec0de204d
- Size of remote file:
- 6.2 kB
- SHA256:
- e07b818b5dc28cec165273fb76a65581e4b4ddf6c8a9ac87c046061b842cec3a
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