Instructions to use djroytburg/auditbench-llama33-70b-graft-sft-aw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use djroytburg/auditbench-llama33-70b-graft-sft-aw with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-70B") model = PeftModel.from_pretrained(base_model, "djroytburg/auditbench-llama33-70b-graft-sft-aw") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6df8705189188cc3885c3af5440a18acf45eacc60cc61185c8020009e4d6169
- Size of remote file:
- 6.63 GB
- SHA256:
- 4ec150161f96b6026f4c595f73979d3585ba23aba80fcbb64a4c4bb4636c0a28
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