# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM
processor = AutoProcessor.from_pretrained("zjunlp/HalDet-llava-7b")
model = AutoModelForCausalLM.from_pretrained("zjunlp/HalDet-llava-7b")Quick Links
HalDet-LLaVA
HalDet-LLaVA is designed for multimodal hallucination detection, trained on the MHaluBench training dataset, achieving detection performance close to that of using GPT4-Vision.
HalDet-LLaVA is trained on the MHaluBench training set using LLaVA-v1.5, specific parameters can be found in the file finetune_task_lora.sh.
We trained HalDet-LLaVA on 1-A800 in 1 hour. If you don"t have enough GPU resources, we will soon provide model distributed training scripts.
You can inference our HalDet-LLaVA by using inference.py
To view more detailed information about HalDet-LLaVA and the train dataset, please refer to the EasyDetect and readme
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zjunlp/HalDet-llava-7b")