KarmaVLM-Qwen1_5-7B

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👏 Introduction

KarmaVLM is a family of high efficiency and powerful visual language model (VLM) pretrained with interleaved image-text data at scale, enabling content comprehension, recognition, and multi-round conversations about images.

🎉 News

• [2024/02] KarmaVLM is released.

⚡️Features

KarmaVLM offers the following 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.

👨‍💻 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 \
       --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 \
  

📋 License

This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses. The content of this project itself is licensed under the Apache license 2.0.

🙇‍ Architecture

We build our project based on LLaVA: Large Language and Vision Assistant.