Instructions to use FatCat87/taopanda-3_c71cc5c5-b4a1-43b5-93ac-4f9fe77ee451 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use FatCat87/taopanda-3_c71cc5c5-b4a1-43b5-93ac-4f9fe77ee451 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2-0.5B") model = PeftModel.from_pretrained(base_model, "FatCat87/taopanda-3_c71cc5c5-b4a1-43b5-93ac-4f9fe77ee451") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d69c1baecedd200b1d745a2a577d4907c88a93a7c14bc74669f8092cc9d383b1
- Size of remote file:
- 6.2 kB
- SHA256:
- f04d755670304ab095fd45492f20143d62f39ffe1ec37df3c969b97f2d5838d8
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