This is a placeholder model card for the RerankerModel.
This re-ranker model is fine-tuned on the Llama-3.1-8B model, utilizing the core implementation and the augmented MS MARCO passage ranking dataset from the Tevatron repository. The primary goal of this fine-tuning is to enable mechanistic interpretability studies on dense re-ranking models.
The model is fine-tuned with a LoRA rank of 8 and trained for 0.4 epochs.
Instructions on how to use the model.
The code for finetuning the model and using the model for inference can be seen from the tevatron repository:
https://github.com/texttron/tevatron/tree/main/examples/rankllamaInformation about the training data used.
Evaluation metrics and results.
