Instructions to use djroytburg/auditbench-llama33-70b-graft-sft-hc 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-hc 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-hc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 461e24ba722fc5e87a8b450043cc1542c1fbe5fbe46eef72938c01d6b8572f28
- Size of remote file:
- 6.63 GB
- SHA256:
- b4c2ea094aab4fb0c7aecc8435edbc47b329b85a005e34878483b1774d60698c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.