Model Card for Snoopy 1.0
This model aims to detect visual manipulation in bar charts.
Model Details
Model Description
- 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
learning_rate = 1e-5 bf16 = True num_train_epochs = 1 optim = "adamw_torch" per_device_train_batch_size = 3 gradient_accumulation_steps = 16 gradient_checkpointing = True
LoRA config: rank = 32, lora_alpha = 32, Using rank stabilized lora target_modules=[q_proj, out_proj, v_proj, k_proj, down_proj, up_proj, o_proj, gate_proj] lora_dropout=0.05, bias="none"
Training Hyperparameters
- Training regime: bf16 non-mixed precision
Citation
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.
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