richardyoung/ dots.mocr:Q8_0

94 Downloads Updated 3 weeks ago

dots.mocr (3B) multilingual document OCR: pages, invoices, forms and tables. Image input works.

vision

ollama run richardyoung/dots.mocr:Q8_0

curl http://localhost:11434/api/chat \
  -d '{
    "model": "richardyoung/dots.mocr:Q8_0",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='richardyoung/dots.mocr:Q8_0',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'richardyoung/dots.mocr:Q8_0',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Details

Updated 3 weeks ago

3 weeks ago

b0560f46e9ea · 4.4GB

model

archqwen2

·

parameters1.78B

·

quantizationQ8_0

1.9GB

projector

archclip

·

parameters1.26B

·

quantizationF16

2.5GB

Readme

dots.mocr

dots.mocr (3B) multilingual document OCR: pages, invoices, forms and tables. Image input works.

🚀 Overview

GGUF build of dots-studio/dots.mocr, a 3B multilingual document OCR model for pages, invoices, forms and tables that also answers simple questions about images. It ships with its vision projector, so image input works in Ollama.

🎯 Key Features

Multilingual document OCR
Pages, invoices, forms and tables
Answers simple questions about images
MIT license

🏷️ Available Versions

Tag	Size (+ 2.52 GB projector)	BPW	Notes
latest / Q4_K_M	1.12 GB	4.85	Recommended
Q8_0	1.89 GB	8.5	Near-lossless

💻 Quick Start

ollama run richardyoung/dots.mocr
# then drag an image file into the prompt, or send it in the API "images" field

🛠️ Use Cases

Digitizing scanned documents
Extracting text from invoices and forms
Multilingual OCR pipelines

✅ Verified

Tested through Ollama before upload with a synthetic invoice and a shapes image: it transcribed the whole invoice exactly and named both shapes and their colors.

🔧 Technical Details

Base Model: dots-studio/dots.mocr
Parameters: 3.0B
Context Length: 128K tokens
Quantization: GGUF via llama.cpp; vision projector kept at F16
License: MIT (inherited from the base model)

🙏 Acknowledgments

Base Model: dots-studio
Quantization: llama.cpp

Built & maintained by Richard Young · DeepNeuro

# dots.mocr

dots.mocr (3B) multilingual document OCR: pages, invoices, forms and tables. Image input works.

## 🚀 Overview

GGUF build of [dots-studio/dots.mocr](https://huggingface.co/dots-studio/dots.mocr), a 3B multilingual document OCR model for pages, invoices, forms and tables that also answers simple questions about images. It ships with its vision projector, so image input works in Ollama.

## 🎯 Key Features

- Multilingual document OCR
- Pages, invoices, forms and tables
- Answers simple questions about images
- MIT license

## 🏷️ Available Versions

| Tag | Size (+ 2.52 GB projector) | BPW | Notes |
|---|---|---|---|
| latest / Q4_K_M | 1.12 GB | 4.85 | Recommended |
| Q8_0 | 1.89 GB | 8.5 | Near-lossless |

## 💻 Quick Start

```bash
ollama run richardyoung/dots.mocr
# then drag an image file into the prompt, or send it in the API "images" field
```

## 🛠️ Use Cases

- Digitizing scanned documents
- Extracting text from invoices and forms
- Multilingual OCR pipelines

## ✅ Verified

Tested through Ollama before upload with a synthetic invoice and a shapes image: it transcribed the whole invoice exactly and named both shapes and their colors.

## 🔧 Technical Details

- **Base Model:** [dots-studio/dots.mocr](https://huggingface.co/dots-studio/dots.mocr)
- **Parameters:** 3.0B
- **Context Length:** 128K tokens
- **Quantization:** GGUF via llama.cpp; vision projector kept at F16
- **License:** MIT (inherited from the base model)

## 🙏 Acknowledgments

- **Base Model:** dots-studio
- **Quantization:** llama.cpp

*Built & maintained by [Richard Young](https://deepneuro.ai/richard) · DeepNeuro*

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