![NuExtract3 released: open-weight 4B VLM for Markdown, OCR and structured extraction (self-hostable) [P]](https://www.digitado.com.br/wp-content/uploads/2026/05/pm2xbooyxn2h1-trY8GF-978x400.png "NuExtract3 released: open-weight 4B VLM for Markdown, OCR and structured extraction (self-hostable) [P]")
NuExtract3 released: open-weight 4B VLM for Markdown, OCR and structured extraction (self-hostable) [P]
![NuExtract3 released: open-weight 4B VLM for Markdown, OCR and structured extraction (self-hostable) [P]](https://preview.redd.it/pm2xbooyxn2h1.png?width=140&height=78&auto=webp&s=30ec102187e4eb4582c79f124c41b8a3c129d1fd "NuExtract3 released: open-weight 4B VLM for Markdown, OCR and structured extraction (self-hostable) [P]") |
Disclaimer: I work for Numind, the company behind this open-weight model We just released a 4B model based on Qwen3.5-4B, under Apache-2.0 license. The goal is to make information extraction from complex documents more practical with an open model: PDFs, screenshots, forms, tables, receipts, invoices, multi-page documents, and other visually structured inputs. Try it, we have a huggingface space that is completely free (you don’t even have to sign-up): https://huggingface.co/spaces/numind/NuExtract3 If you ever used NuMarkdown, NuExtract3 is the successor. There are some examples to guide you. Feel free to re-use this model for any task. A few things it is designed for:
It was trained on a node of 8xH100 for 3 days to train on as much context as we could, so it should perform fairly well even on long document. For Markdown, we’d still recommend going page by page for the best results and inference speed, since you can parallelize better this way. It’s very easy to self-host, since we provide fairly extensive documentation, Safetensors, GGUF and MLX weights. With as little as 4GB of VRAM, you should be good to go. We provide multiple quantizations (GPTQ, W8A8, FP8, Q4, Q6…) so you should be able to run it anywhere. We mostly tried vLLM, SGLang, llama.cpp. We have a blog post and a pretty decent model card:
I’m currently writing a paper on this model so I’ll post it as soon as it’s accepted. It’s not yet on Arxiv yet as it has been submitted in a peer-review journal/conference. I’ll try to answer as many questions as possible if you have any. We would really appreciate feedback from the community. We also have a discord if you’re interested submitted by /u/Gailenstorm |
