yasserrmd/Nanonets-OCR2-3B (8-bit)

Base model: nanonets/Nanonets-OCR2-3B Type: Multimodal OCR & document understanding (images → structured text, tables, LaTeX, captions). Precision: 8-bit quantized for efficient inference. Params: ~3B Format: GGUF / Ollama compatible

⚙️ Usage (Ollama)

ollama pull yasserrmd/Nanonets-OCR2-3B:q8_0
ollama run yasserrmd/Nanonets-OCR2-3B:q8_0

Example prompt:

Extract all text, tables, and equations from the uploaded document image.
Return tables in HTML and equations in LaTeX.

You can also use it via API:

import requests
requests.post("http://localhost:11434/api/generate",
              json={"model":"yasserrmd/Nanonets-OCR2-3B:q8_0",
                    "prompt":"<your prompt here>"})

📘 Notes

• Original documentation, evaluation, and architecture: Hugging Face Model Page →
• Use high-resolution input images for better OCR accuracy.
• Quantization improves performance with minimal quality loss.
• Best suited for document parsing, forms, and scanned PDFs.