chart-misinformation-detection/llava-1.6-mistral-7b-snoopy-1.0-post-finetune-full-folder

Model Card for Snoopy 1.0

This model aims to detect visual manipulation in bar charts.

Model Details

Model Description

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• —Developed by: Arif Syraj
• —Model type: Multi-Modal LLM
• —Finetuned from model: llava-1.6-mistral-7b

How to Get Started with the Model

This is not a HuggingFace-based model, please refer to this Colab notebook to run inference. Only works on GPU.

Training Details

Finetuned with LoRA for 1 epoch on ~2700 images of misleading and non misleading bar charts

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> learningrate = 1e-5 bf16 = True numtrainepochs = 1 optim = "adamwtorch" perdevicetrainbatchsize = 3 gradientaccumulationsteps = 16 gradient_checkpointing = True

LoRA config: rank = 32, loraalpha = 32, Using rank stabilized lora targetmodules=[qproj, outproj, vproj, kproj, downproj, upproj, oproj, gateproj] lora_dropout=0.05, bias="none"

Training Hyperparameters

• —Training regime: bf16 non-mixed precision

Citation

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BibTeX:

• —Liu, Haotian, Li, Chunyuan, Li, Yuheng, Li, Bo, Zhang, Yuanhan, Shen, Sheng, & Lee, Yong Jae. (2024, January). LLaVA-NeXT: Improved reasoning, OCR, and world knowledge. Retrieved from https://llava-vl.github.io/blog/2024-01-30-llava-next/.

• —Liu, Haotian, Li, Chunyuan, Li, Yuheng, & Lee, Yong Jae. (2023). Improved Baselines with Visual Instruction Tuning. arXiv:2310.03744.

• —Liu, Haotian, Li, Chunyuan, Wu, Qingyang, & Lee, Yong Jae. (2023). Visual Instruction Tuning. NeurIPS.