I am fine-tuning the Bunny-v1_1-Llama-3-8B-V model on a dataset with a task that requires answering "Yes" or "No". However, after fine-tuning, the model only responds with "Yes" or "No" even to questions that are of a different type from those in the fine-tuning dataset. I suspect there might be a mistake in my fine-tuning process; it seems like the code is training from scratch rather than fine-tuning from the pre-trained model. Could anyone help identify the issue? Many thanks.
Here's my fine-tuning lora script (finetune_lora.sh):
I am fine-tuning the Bunny-v1_1-Llama-3-8B-V model on a dataset with a task that requires answering "Yes" or "No". However, after fine-tuning, the model only responds with "Yes" or "No" even to questions that are of a different type from those in the fine-tuning dataset. I suspect there might be a mistake in my fine-tuning process; it seems like the code is training from scratch rather than fine-tuning from the pre-trained model. Could anyone help identify the issue? Many thanks.
Here's my fine-tuning lora script (finetune_lora.sh):
After training, I merge the model using the following script: