@host.today/ht-docker-ai 🚀

Production-ready Docker images for state-of-the-art AI Vision-Language Models. Run powerful multimodal AI locally with GPU acceleration or CPU fallback—no cloud API keys required.

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🎯 What's Included

Model	Parameters	Best For	API
MiniCPM-V 4.5	8B	General vision understanding, image analysis, multi-image	Ollama-compatible
PaddleOCR-VL	0.9B	Document parsing, table extraction, OCR	OpenAI-compatible

📦 Available Images

code.foss.global/host.today/ht-docker-ai:<tag>

Tag	Model	Hardware	Port
minicpm45v / latest	MiniCPM-V 4.5	NVIDIA GPU (9-18GB VRAM)	11434
minicpm45v-cpu	MiniCPM-V 4.5	CPU only (8GB+ RAM)	11434
paddleocr-vl / paddleocr-vl-gpu	PaddleOCR-VL	NVIDIA GPU	8000
paddleocr-vl-cpu	PaddleOCR-VL	CPU only	8000

---

🖼️ MiniCPM-V 4.5

A GPT-4o level multimodal LLM from OpenBMB—handles image understanding, OCR, multi-image analysis, and visual reasoning across 30+ languages.

Quick Start

GPU (Recommended):

docker run -d \
  --name minicpm \
  --gpus all \
  -p 11434:11434 \
  -v ollama-data:/root/.ollama \
  code.foss.global/host.today/ht-docker-ai:minicpm45v

CPU Only:

docker run -d \
  --name minicpm \
  -p 11434:11434 \
  -v ollama-data:/root/.ollama \
  code.foss.global/host.today/ht-docker-ai:minicpm45v-cpu

💡 Pro tip: Mount the volume to persist downloaded models (~5GB). Without it, models re-download on every container start.

API Examples

List models:

curl http://localhost:11434/api/tags

Analyze an image:

curl http://localhost:11434/api/generate -d '{
  "model": "minicpm-v",
  "prompt": "What do you see in this image?",
  "images": ["<base64-encoded-image>"]
}'

Chat with vision:

curl http://localhost:11434/api/chat -d '{
  "model": "minicpm-v",
  "messages": [{
    "role": "user",
    "content": "Describe this image in detail",
    "images": ["<base64-encoded-image>"]
  }]
}'

Hardware Requirements

Variant	VRAM/RAM	Notes
GPU (int4 quantized)	9GB VRAM	Recommended for most use cases
GPU (full precision)	18GB VRAM	Maximum quality
CPU (GGUF)	8GB+ RAM	Slower but accessible

---

📄 PaddleOCR-VL

A specialized 0.9B Vision-Language Model optimized for document parsing. Native support for tables, formulas, charts, and text extraction in 109 languages.

Quick Start

GPU:

docker run -d \
  --name paddleocr \
  --gpus all \
  -p 8000:8000 \
  -v hf-cache:/root/.cache/huggingface \
  code.foss.global/host.today/ht-docker-ai:paddleocr-vl

CPU:

docker run -d \
  --name paddleocr \
  -p 8000:8000 \
  -v hf-cache:/root/.cache/huggingface \
  code.foss.global/host.today/ht-docker-ai:paddleocr-vl-cpu

OpenAI-Compatible API

PaddleOCR-VL exposes a fully OpenAI-compatible /v1/chat/completions endpoint:

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "paddleocr-vl",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64>"}},
        {"type": "text", "text": "Table Recognition:"}
      ]
    }],
    "max_tokens": 8192
  }'

Task Prompts

Prompt	Output	Use Case
OCR:	Plain text	General text extraction
Table Recognition:	Markdown table	Invoices, bank statements, spreadsheets
Formula Recognition:	LaTeX	Math equations, scientific notation
Chart Recognition:	Description	Graphs and visualizations

API Endpoints

Endpoint	Method	Description
/health	GET	Health check with model/device info
/formats	GET	Supported image formats and input methods
/v1/models	GET	List available models
/v1/chat/completions	POST	OpenAI-compatible chat completions
/ocr	POST	Legacy OCR endpoint

Image Input Methods

PaddleOCR-VL accepts images in multiple formats:

// Base64 data URL
"data:image/png;base64,iVBORw0KGgo..."

// HTTP URL
"https://example.com/document.png"

// Raw base64
"iVBORw0KGgo..."

Supported formats: PNG, JPEG, WebP, BMP, GIF, TIFF

Optimal resolution: 1080p–2K. Images are automatically scaled for best results.

Performance

Mode	Speed per Page
GPU (CUDA)	2–5 seconds
CPU	30–60 seconds

---

🐳 Docker Compose

version: '3.8'
services:
  # General vision tasks
  minicpm:
    image: code.foss.global/host.today/ht-docker-ai:minicpm45v
    ports:
      - "11434:11434"
    volumes:
      - ollama-data:/root/.ollama
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped

  # Document parsing / OCR
  paddleocr:
    image: code.foss.global/host.today/ht-docker-ai:paddleocr-vl
    ports:
      - "8000:8000"
    volumes:
      - hf-cache:/root/.cache/huggingface
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped

volumes:
  ollama-data:
  hf-cache:

---

⚙️ Environment Variables

MiniCPM-V 4.5

Variable	Default	Description
MODEL_NAME	minicpm-v	Ollama model to pull on startup
OLLAMA_HOST	0.0.0.0	API bind address
OLLAMA_ORIGINS	*	Allowed CORS origins

PaddleOCR-VL

Variable	Default	Description
MODEL_NAME	PaddlePaddle/PaddleOCR-VL	HuggingFace model ID
SERVER_HOST	0.0.0.0	API bind address
SERVER_PORT	8000	API port

---

🔧 Building from Source

# Clone the repository
git clone https://code.foss.global/host.today/ht-docker-ai.git
cd ht-docker-ai

# Build all images
./build-images.sh

# Run tests
./test-images.sh

---

🏗️ Architecture Notes

Dual-VLM Consensus Strategy

For production document extraction, consider using both models together:

1. Pass 1: MiniCPM-V visual extraction (images → JSON)
2. Pass 2: PaddleOCR-VL table recognition (images → markdown → JSON)
3. Consensus: If results match → Done (fast path)
4. Pass 3+: Additional visual passes if needed

This dual-VLM approach catches extraction errors that single models miss.

Why This Works

• Different architectures: Two independent models cross-validate each other
• Specialized strengths: PaddleOCR-VL excels at tables; MiniCPM-V handles general vision
• Native processing: Both VLMs see original images—no intermediate HTML/structure loss

---

🔍 Troubleshooting

Model download hangs

docker logs -f <container-name>

Model downloads can take several minutes (~5GB for MiniCPM-V).

Out of memory

• GPU: Use the CPU variant or upgrade VRAM
• CPU: Increase container memory: --memory=16g

API not responding

1. Check container health: docker ps
2. Review logs: docker logs <container>
3. Verify port: curl localhost:11434/api/tags or curl localhost:8000/health

Enable NVIDIA GPU support on host

# Install NVIDIA Container Toolkit
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

---

License and Legal Information

This repository contains open-source code licensed under the MIT License. A copy of the license can be found in the LICENSE file.

Please note: The MIT License does not grant permission to use the trade names, trademarks, service marks, or product names of the project, except as required for reasonable and customary use in describing the origin of the work and reproducing the content of the NOTICE file.

Trademarks

This project is owned and maintained by Task Venture Capital GmbH. The names and logos associated with Task Venture Capital GmbH and any related products or services are trademarks of Task Venture Capital GmbH or third parties, and are not included within the scope of the MIT license granted herein.

Use of these trademarks must comply with Task Venture Capital GmbH's Trademark Guidelines or the guidelines of the respective third-party owners, and any usage must be approved in writing. Third-party trademarks used herein are the property of their respective owners and used only in a descriptive manner, e.g. for an implementation of an API or similar.

Company Information

Task Venture Capital GmbH Registered at District Court Bremen HRB 35230 HB, Germany

For any legal inquiries or further information, please contact us via email at hello@task.vc.

By using this repository, you acknowledge that you have read this section, agree to comply with its terms, and understand that the licensing of the code does not imply endorsement by Task Venture Capital GmbH of any derivative works.