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---
datasets:
- chart-misinformation-detection/MISCHA-QA-v1
language:
- en
tags:
- LLaVA
- misinformation
---
# Model Card for Snoopy 1.0
This model aims to detect visual manipulation in bar charts.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **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](https://colab.research.google.com/drive/1UpnztYv46faXj-kmFpL_GAbOCjP2u6zM?usp=sharing)
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. -->
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
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**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/](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*. |