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:
- 415d787aa1d46bfe7ed50b6c03316800ab0c7c139d7b385be87d00fa1420167d
- Size of remote file:
- 26.2 MB
- SHA256:
- 99f85eb80651f83b6f992b8781133e612eeefb184e648aa43720ea337be35f70
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