Instructions to use tryingpro/1b4d483a-975c-40d4-b21e-9d10d3f2882d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tryingpro/1b4d483a-975c-40d4-b21e-9d10d3f2882d with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/tiny-random-LlamaForCausalLM") model = PeftModel.from_pretrained(base_model, "tryingpro/1b4d483a-975c-40d4-b21e-9d10d3f2882d") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cb04b2993b98a0c2e2a472b066b113debe4322bd1d5091c1713095b2a572a5b
- Size of remote file:
- 6.84 kB
- SHA256:
- bc2b1424c99740f83419d907f3d4b7f258d11f9941873a40f63dd91482fa5beb
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