Awesome
VLGuard
[Website] [Paper] [Data] [🤗Weights]
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models.
Updates
- [2024/07/09] We released the evaluation code for VLGuard test set.
- [2024/06/19] We released the fine-tuned model weights that we used for experiments.
- [2024/05/01] VLGuard is accepted to ICML 2024!
- [2024/02/06] We released arXiv and data for VLGuard. With our safety fine-tuning, the we substantially improve the safety of vision large language models while maintaining the helpfulness.
Dataset
You can find the dataset at Huggingface. train.json and test.json are the meta data of VLGuard and the images are in train.zip and test.zip.
Evaluation
After setting up the datasets, you can run the following commands to evaluate three subsets of VLGuard: safe_safes, safe_unsafes, and unsafes:
CUDA_VISIBLE_DEVICES=0 python VLGuard_eval.py --dataset unsafes --engine llava15-7b --metaDir /path/to/test.json --imageDir /path/to/VLGuard/test
CUDA_VISIBLE_DEVICES=0 python VLGuard_eval.py --dataset safe_unsafes --engine llava15-7b --metaDir /path/to/test.json --imageDir /path/to/VLGuard/test
CUDA_VISIBLE_DEVICES=0 python VLGuard_eval.py --dataset safe_safes --engine llava15-7b --metaDir /path/to/test.json --imageDir /path/to/VLGuard/test
The scripts will print out the ASR for safe_unsafes, and unsafes with string match (keywords here). The generated predictions will be saved to results folder.
To evaluate the helpfulness with safe_safes subset, run:
OPENAI_API_KEY="" # your OpenAI API key
python gpt4_evaluator.py --file_path results/safe_safes/{the_model_to_evaluate}.json --image_path /path/to/VLGuard/test --reference_path ./data/gpt4_safe_safes.json --output_path /path/to/save/results
It will calculate the win rate against GPT-4V.
Model Weights
We release the weights below. You can use them in exactly the same way as the original LLaVA.
Weights from Mixed Fine-tuning
| Model | Original VLLM | Fine-tuning | 🤗 Checkpoint |
|---|---|---|---|
| LLaVA-v1.5-7B-Mixed | LLaVA-v1.5-7B | Full FT | ys-zong/llava-v1.5-7b-Mixed |
| LLaVA-v1.5-7B-Mixed-LoRA | LLaVA-v1.5-7B | LoRA | ys-zong/llava-v1.5-7b-Mixed-lora |
| LLaVA-v1.5-13B-Mixed | LLaVA-v1.5-13B | Full FT | ys-zong/llava-v1.5-13b-Mixed |
| LLaVA-v1.5-13B-Mixed-LoRA | LLaVA-v1.5-13B | LoRA | ys-zong/llava-v1.5-13b-Mixed-lora |
Weights from Post-hoc Fine-tuning
| Model | Original VLLM | Fine-tuning | 🤗 Checkpoint |
|---|---|---|---|
| LLaVA-v1.5-7B-Posthoc | LLaVA-v1.5-7B | Full FT | ys-zong/llava-v1.5-7b-Posthoc |
| LLaVA-v1.5-7B-Posthoc-LoRA | LLaVA-v1.5-7B | LoRA | ys-zong/llava-v1.5-7b-Posthoc-lora |
| LLaVA-v1.5-13B-Posthoc | LLaVA-v1.5-13B | Full FT | ys-zong/llava-v1.5-13b-Posthoc |
| LLaVA-v1.5-13B-Posthoc-LoRA | LLaVA-v1.5-13B | LoRA | ys-zong/llava-v1.5-13b-Posthoc-lora |
We have also released the weights of "Clean" LLaVA-v1.5 that we re-trained after removing the harmful samples from the training data (Table 1).
| Model | LLM | Fine-tuning | 🤗 Checkpoint |
|---|---|---|---|
| LLaVA-v1.5-7B-Clean | Vicuna-7B | Full FT | ys-zong/llava-v1.5-7b-Clean |
| LLaVA-v1.5-7B-Clean-LoRA | Vicuna-7B | LoRA | ys-zong/llava-v1.5-7b-Clean-lora |
| LLaVA-v1.5-13B-Clean | Vicuna-13B | Full FT | ys-zong/llava-v1.5-13b-Clean |
| LLaVA-v1.5-13B-Clean-LoRA | Vicuna-13B | LoRA | ys-zong/llava-v1.5-13b-Clean-lora |
Usage
To fine-tune LLaVA or MiniGPT-v2, you can first run
python convert_to_llava_format.py
to convert VLGuard to LLaVA data format and follow their fine-tuning scripts to do the fine-tuning.
Citation
@article{zong2023safety,
title={Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models},
author={Zong, Yongshuo and Bohdal, Ondrej and Yu, Tingyang and Yang, Yongxin and Hospedales Timothy},
journal={arXiv preprint arXiv:2402.02207},
year={2024}
}