---
license: apache-2.0
language:
- en
pipeline_tag: visual-question-answering
---

# Algea-VE: A Tiny Multimodal Language Model with Only 0.8B Parameters 


Algea-ve is trained on the LAION-CC-SBU dataset using [algea-550M-base](https://huggingface.co/PhelixZhen/Algae-550M-base) as the base model and fine-tuned on llava_v1_5_mix665k. It uses CLIP ViT-L/14-336 as the visual encoder. The model is very small, requiring only 32GB of VRAM for fine-tuning and 3GB for inference.

Due to insufficient training of the base model, the current model has some issues with hallucinations and repetition. To address this, I am training a new model that will maintain the same size but offer better performance. 

This model is built based on the llavaphi project. To use the model, please click [here](https://github.com/phelixzhen/Algea-VE).