🧘🏻‍♂️ KarmaVLM (相生)

👏 Introduction

🎉 News

• [2024/02] KarmaVLM is released.

⚡️Features

• High Efficiency: KarmaVLM focuses on exploring the capabilities of small parametric quantitative models on multimodal tasks. So, KarmaVLM can be efficiently deployed on most GPU cards and personal computers, and even on end devices such as mobile phones.
• Multi-round text-image conversations: KarmaVLM can take both text and images as inputs and produce text outputs. Currently, it supports multi-round visual question answering with one image.
• Strong image comprehension: KarmaVLM is adept at analyzing visuals, making it an efficient tool for tasks like extracting, organizing, and summarizing information from images.

🔥Model Zoo

👨‍💻 Quick Start

Requirements and Installation

git clone https://github.com/X-D-Lab/KarmaVLM.git
cd KarmaVLM

conda create -n karmavlm python=3.10 -y
conda activate karmavlm

pip install --upgrade pip  # enable PEP 660 support
pip install -e .
pip install -e ".[train]"
pip install flash-attn --no-build-isolation

🌏 Demo

1. CLI Inference

   python -m llava.serve.cli \
       --model-path /path/to/karmavlm/model \
       --model-type qwen \
       --image-file /path/to/the/test/image

2. Gradio Web UI

• Starting the Controller

  python -m llava.serve.gradio_web_server \
  --controller http://localhost:10000 \
  --model-list-mode reload
  --share ##(optional)

• Launching the Gradio Web Server

  python -m llava.serve.model_worker \
  --host 0.0.0.0 \
  --controller http://localhost:10000 \
  --port 40000 \
  --worker http://localhost:40000 \
  --model-path /path/to/karmavlm/model \
  --model-type qwen

📋 License

🙇‍ Architecture