Instructions to use arumaekawa/rstdt-7b-span with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use arumaekawa/rstdt-7b-span with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "arumaekawa/rstdt-7b-span") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2850962c802b0ceba75e1b5698aeb4e8e8c4be305daf703f776973505ebf13be
- Size of remote file:
- 640 MB
- SHA256:
- 8799daae1aef33522359fa9a9b4f44945d161bad8cb8224f4bb9aa1fc3cce808
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