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:
- 1c31acb96e42438ebc1a8eeedc3126becdf63c750f9f323f3399a102636b1fed
- Size of remote file:
- 26.2 MB
- SHA256:
- 5bd5a6dcc0aa59a0ef439514af5095e9b16d7c6c51e0898ec45ebdd681be2237
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