Files

2026-01-19 11:51:23 +00:00

15 KiB

Raw Blame History

@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.

🔥 Four VLMs, one registry. From lightweight document OCR to GPT-4o-level vision understanding—pick the right tool for your task.

Issue Reporting and Security

For reporting bugs, issues, or security vulnerabilities, please visit community.foss.global/. This is the central community hub for all issue reporting. Developers who sign and comply with our contribution agreement and go through identification can also get a code.foss.global/ account to submit Pull Requests directly.

🎯 What's Included

Model	Parameters	Best For	API	Port
MiniCPM-V 4.5	8B	General vision understanding, multi-image analysis	Ollama-compatible	11434
PaddleOCR-VL	0.9B	Document parsing, table extraction, structured OCR	OpenAI-compatible	8000
Nanonets-OCR-s	~4B	Document OCR with semantic markdown output	OpenAI-compatible	8000
Qwen3-VL-30B	30B (A3B)	Advanced visual agents, code generation from images	Ollama-compatible	11434

📦 Quick Reference: All 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
`nanonets-ocr`	Nanonets-OCR-s	NVIDIA GPU (8-10GB VRAM)	8000
`qwen3vl`	Qwen3-VL-30B-A3B	NVIDIA GPU (~20GB VRAM)	11434

🖼️ 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

🔍 Nanonets-OCR-s

A Qwen2.5-VL-3B model fine-tuned specifically for document OCR. Outputs structured markdown with semantic HTML tags—perfect for preserving document structure.

Key Features

📝 Semantic output: Tables → HTML, equations → LaTeX, watermarks/page numbers → tagged
🌍 Multilingual: Inherits Qwen's broad language support
⚡ Efficient: ~8-10GB VRAM, runs great on consumer GPUs
🔌 OpenAI-compatible: Drop-in replacement for existing pipelines

Quick Start

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

API Usage

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nanonets/Nanonets-OCR-s",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64>"}},
        {"type": "text", "text": "Extract the text from the above document as if you were reading it naturally. Return the tables in html format. Return the equations in LaTeX representation."}
      ]
    }],
    "temperature": 0.0,
    "max_tokens": 4096
  }'

Output Format

Nanonets-OCR-s returns markdown with semantic tags:

Element	Output Format
Tables	`<table>...</table>` (HTML)
Equations	$...$ (LaTeX)
Images	`<img>description</img>`
Watermarks	`<watermark>OFFICIAL COPY</watermark>`
Page numbers	`<page_number>14</page_number>`

Performance

Metric	Value
Speed	3–8 seconds per page
VRAM	~8-10GB

🧠 Qwen3-VL-30B-A3B

The most powerful Qwen vision model—30B parameters with 3B active (MoE architecture). Handles complex visual reasoning, code generation from screenshots, and visual agent capabilities.

Key Features

🚀 256K context (expandable to 1M tokens!)
🤖 Visual agent capabilities — can plan and execute multi-step tasks
💻 Code generation from images — screenshot → working code
🎯 State-of-the-art visual reasoning

Quick Start

docker run -d \
  --name qwen3vl \
  --gpus all \
  -p 11434:11434 \
  -v ollama-data:/root/.ollama \
  code.foss.global/host.today/ht-docker-ai:qwen3vl

Then pull the model (one-time, ~20GB):

docker exec qwen3vl ollama pull qwen3-vl:30b-a3b

API Usage

curl http://localhost:11434/api/chat -d '{
  "model": "qwen3-vl:30b-a3b",
  "messages": [{
    "role": "user",
    "content": "Analyze this screenshot and write the code to recreate this UI",
    "images": ["<base64-encoded-image>"]
  }]
}'

Hardware Requirements

Requirement	Value
VRAM	~20GB (Q4_K_M quantization)
Context	256K tokens default

🐳 Docker Compose

Run multiple VLMs together for maximum flexibility:

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 (table specialist)
  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

  # Document OCR with semantic output
  nanonets:
    image: code.foss.global/host.today/ht-docker-ai:nanonets-ocr
    ports:
      - "8001:8000"
    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 & Qwen3-VL (Ollama-based)

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 & Nanonets-OCR (vLLM-based)

Variable	Default	Description
`MODEL_NAME`	Model-specific	HuggingFace model ID
`HOST`	`0.0.0.0`	API bind address
`PORT`	`8000`	API port
`MAX_MODEL_LEN`	`8192`	Maximum sequence length
`GPU_MEMORY_UTILIZATION`	`0.9`	GPU memory usage (0-1)

🏗️ Architecture Notes

Dual-VLM Consensus Strategy

For production document extraction, consider using multiple models together:

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

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

Why Multi-Model Works

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

Model Selection Guide

Task	Recommended Model
General image understanding	MiniCPM-V 4.5
Table extraction from documents	PaddleOCR-VL
Document OCR with structure preservation	Nanonets-OCR-s
Complex visual reasoning / code generation	Qwen3-VL-30B
Multi-image analysis	MiniCPM-V 4.5
Visual agent tasks	Qwen3-VL-30B

🔧 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

🔍 Troubleshooting

Model download hangs

docker logs -f <container-name>

Model downloads can take several minutes (~5GB for MiniCPM-V, ~20GB for Qwen3-VL).

Out of memory

GPU: Use a lighter model variant or upgrade VRAM
CPU: Increase container memory: --memory=16g

API not responding

Check container health: docker ps
Review logs: docker logs <container>
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

GPU Memory Contention (Multi-Model)

When running multiple VLMs on a single GPU:

vLLM and Ollama both need significant GPU memory
Single GPU: Run services sequentially (stop one before starting another)
Multi-GPU: Assign each service to a different GPU via CUDA_VISIBLE_DEVICES

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.

15 KiB Raw Blame History Unescape Escape

@host.today/ht-docker-ai 🚀

Issue Reporting and Security

🎯 What's Included

📦 Quick Reference: All Available Images

🖼️ MiniCPM-V 4.5

Quick Start

API Examples

Hardware Requirements

📄 PaddleOCR-VL

Quick Start

OpenAI-Compatible API

Task Prompts

API Endpoints

Image Input Methods

Performance

🔍 Nanonets-OCR-s

Key Features

Quick Start

API Usage

Output Format

Performance

🧠 Qwen3-VL-30B-A3B

Key Features

Quick Start

API Usage

Hardware Requirements

🐳 Docker Compose

⚙️ Environment Variables

MiniCPM-V 4.5 & Qwen3-VL (Ollama-based)

PaddleOCR-VL & Nanonets-OCR (vLLM-based)

🏗️ Architecture Notes

Dual-VLM Consensus Strategy

Why Multi-Model Works

Model Selection Guide

🔧 Building from Source

🔍 Troubleshooting

Model download hangs

Out of memory

API not responding

Enable NVIDIA GPU support on host

GPU Memory Contention (Multi-Model)

License and Legal Information

Trademarks

Company Information

15 KiB

Raw Blame History