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fix(readme): update README to document Nanonets-OCR2-3B (replaces Nanonets-OCR-s), adjust VRAM and context defaults, expand feature docs, and update examples/test command

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Jürgen Kunz
2026-01-19 21:28:26 +00:00
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readme.md
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@@ -2,7 +2,7 @@
Production-ready Docker images for state-of-the-art AI Vision-Language Models. Run powerful multimodal AI locally with GPU acceleration—**no cloud API keys required**.
> 🔥 **Three VLMs, one registry.** From lightweight document OCR to GPT-4o-level vision understanding—pick the right tool for your task.
> 🔥 **Three VLMs, one registry.** From high-performance document OCR to GPT-4o-level vision understanding—pick the right tool for your task.
## Issue Reporting and Security
@@ -15,7 +15,7 @@ For reporting bugs, issues, or security vulnerabilities, please visit [community
| Model | Parameters | Best For | API | Port | VRAM |
|-------|-----------|----------|-----|------|------|
| **MiniCPM-V 4.5** | 8B | General vision understanding, multi-image analysis | Ollama-compatible | 11434 | ~9GB |
| **Nanonets-OCR-s** | ~4B | Document OCR with semantic markdown output | OpenAI-compatible | 8000 | ~10GB |
| **Nanonets-OCR2-3B** | ~3B | Document OCR with semantic markdown, LaTeX, flowcharts | OpenAI-compatible | 8000 | ~12-16GB |
| **Qwen3-VL-30B** | 30B (A3B) | Advanced visual agents, code generation from images | Ollama-compatible | 11434 | ~20GB |
---
@@ -29,7 +29,7 @@ code.foss.global/host.today/ht-docker-ai:<tag>
| Tag | Model | Runtime | Port | VRAM |
|-----|-------|---------|------|------|
| `minicpm45v` / `latest` | MiniCPM-V 4.5 | Ollama | 11434 | ~9GB |
| `nanonets-ocr` | Nanonets-OCR-s | vLLM | 8000 | ~10GB |
| `nanonets-ocr` | Nanonets-OCR2-3B | vLLM | 8000 | ~12-16GB |
| `qwen3vl` | Qwen3-VL-30B-A3B | Ollama | 11434 | ~20GB |
---
@@ -38,6 +38,13 @@ code.foss.global/host.today/ht-docker-ai:<tag>
A GPT-4o level multimodal LLM from OpenBMB—handles image understanding, OCR, multi-image analysis, and visual reasoning across **30+ languages**.
### ✨ Key Features
- 🌍 **Multilingual:** 30+ languages supported
- 🖼️ **Multi-image:** Analyze multiple images in one request
- 📊 **Versatile:** Charts, documents, photos, diagrams
- ⚡ **Efficient:** Runs on consumer GPUs (9GB VRAM)
### Quick Start
```bash
@@ -83,21 +90,22 @@ curl http://localhost:11434/api/chat -d '{
| Mode | VRAM Required |
|------|---------------|
| int4 quantized | 9GB |
| Full precision (bf16) | 18GB |
| int4 quantized | ~9GB |
| Full precision (bf16) | ~18GB |
---
## 🔍 Nanonets-OCR-s
## 🔍 Nanonets-OCR2-3B
A **Qwen2.5-VL-3B** model fine-tuned specifically for document OCR. Outputs structured markdown with semantic HTML tags—perfect for preserving document structure.
The **latest Nanonets document OCR model** (October 2025 release)—based on Qwen2.5-VL-3B, fine-tuned specifically for document extraction with significant improvements over the original OCR-s.
### Key Features
### ✨ Key Features
- 📝 **Semantic output:** Tables → HTML, equations → LaTeX, watermarks/page numbers → tagged
- 📝 **Semantic output:** Tables → HTML, equations → LaTeX, flowcharts → structured markup
- 🌍 **Multilingual:** Inherits Qwen's broad language support
- ⚡ **Efficient:** ~10GB VRAM, runs great on consumer GPUs
- 📄 **30K context:** Handle large, multi-page documents
- 🔌 **OpenAI-compatible:** Drop-in replacement for existing pipelines
- 🎯 **Improved accuracy:** Better semantic tagging and LaTeX equation extraction vs. OCR-s
### Quick Start
@@ -116,7 +124,7 @@ docker run -d \
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "nanonets/Nanonets-OCR-s",
"model": "nanonets/Nanonets-OCR2-3B",
"messages": [{
"role": "user",
"content": [
@@ -131,7 +139,7 @@ curl http://localhost:8000/v1/chat/completions \
### Output Format
Nanonets-OCR-s returns markdown with semantic tags:
Nanonets-OCR2-3B returns markdown with semantic tags:
| Element | Output Format |
|---------|---------------|
@@ -140,13 +148,14 @@ Nanonets-OCR-s returns markdown with semantic tags:
| Images | `<img>description</img>` |
| Watermarks | `<watermark>OFFICIAL COPY</watermark>` |
| Page numbers | `<page_number>14</page_number>` |
| Flowcharts | Structured markup |
### Performance
### Hardware Requirements
| Metric | Value |
|--------|-------|
| Speed | 3–8 seconds per page |
| VRAM | ~10GB |
| Config | VRAM |
|--------|------|
| 30K context (default) | ~12-16GB |
| Speed | ~3-8 seconds per page |
---
@@ -154,7 +163,7 @@ Nanonets-OCR-s returns markdown with semantic tags:
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
### ✨ Key Features
- 🚀 **256K context** (expandable to 1M tokens!)
- 🤖 **Visual agent capabilities** — can plan and execute multi-step tasks
@@ -204,7 +213,6 @@ curl http://localhost:11434/api/chat -d '{
Run multiple VLMs together for maximum flexibility:
```yaml
version: '3.8'
services:
# General vision tasks
minicpm:
@@ -259,10 +267,10 @@ volumes:
| Variable | Default | Description |
|----------|---------|-------------|
| `MODEL_NAME` | `nanonets/Nanonets-OCR-s` | HuggingFace model ID |
| `MODEL_NAME` | `nanonets/Nanonets-OCR2-3B` | HuggingFace model ID |
| `HOST` | `0.0.0.0` | API bind address |
| `PORT` | `8000` | API port |
| `MAX_MODEL_LEN` | `8192` | Maximum sequence length |
| `MAX_MODEL_LEN` | `30000` | Maximum sequence length |
| `GPU_MEMORY_UTILIZATION` | `0.9` | GPU memory usage (0-1) |
---
@@ -283,7 +291,7 @@ 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:** Nanonets-OCR-s excels at document structure; MiniCPM-V handles general vision
- **Specialized strengths:** Nanonets-OCR2-3B excels at document structure; MiniCPM-V handles general vision
- **Native processing:** All VLMs see original images—no intermediate structure loss
### Model Selection Guide
@@ -291,10 +299,11 @@ This dual-VLM approach catches extraction errors that single models miss.
| Task | Recommended Model |
|------|-------------------|
| General image understanding | MiniCPM-V 4.5 |
| Document OCR with structure preservation | Nanonets-OCR-s |
| Document OCR with structure preservation | Nanonets-OCR2-3B |
| Complex visual reasoning / code generation | Qwen3-VL-30B |
| Multi-image analysis | MiniCPM-V 4.5 |
| Visual agent tasks | Qwen3-VL-30B |
| Large documents (30K+ tokens) | Nanonets-OCR2-3B |
---
@@ -309,7 +318,7 @@ cd ht-docker-ai
./build-images.sh
# Run tests
./test-images.sh
pnpm test
```
---