Instructions to use mamung/29b84e13-2f1c-406f-9e7e-fd35b6e5ff49 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mamung/29b84e13-2f1c-406f-9e7e-fd35b6e5ff49 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("openlm-research/open_llama_3b") model = PeftModel.from_pretrained(base_model, "mamung/29b84e13-2f1c-406f-9e7e-fd35b6e5ff49") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40c9d68a5cb258de96d17a72a3d0b34615beca56d89ae3d523f9053ce30e6a4d
- Size of remote file:
- 50.9 MB
- SHA256:
- 4a79886202abcc0c1ffbb4ff6189af64ef92cab2c1d75ee0e50f3410c952e6f6
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