Numind released NuExtract3, a 4B open-weight vision-language model based on Qwen3.5-4B, designed for converting document images to Markdown, OCR, and structured data extraction. It is Apache-2.0 licensed and self-hostable with quantized versions for low VRAM.
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](https://huggingface.co/spaces/numind/NuExtract3) If you ever used [NuMarkdown](https://huggingface.co/numind/NuMarkdown-8B-Thinking), NuExtract3 is the successor. There are some examples to guide you. Feel free to re-use this model for any task. https://preview.redd.it/pm2xbooyxn2h1.png?width=1672&format=png&auto=webp&s=1a8a7b262190c8325159496dae98c3d2dfab493c https://preview.redd.it/b5z7ylfzxn2h1.png?width=1758&format=png&auto=webp&s=a07b3abd6e5065c2635de047bdf154357f903e4c [](https://preview.redd.it/nuextract3-released-open-weight-4b-vlm-for-markdown-ocr-and-v0-cdflrhrexn2h1.png?width=1672&format=png&auto=webp&s=f5590cf684a45e4cf2fcd9b1e2929cba7146634e) [](https://preview.redd.it/nuextract3-released-open-weight-4b-vlm-for-markdown-ocr-and-v0-q3dn99ufxn2h1.png?width=1758&format=png&auto=webp&s=3c987fda617d23a6e51ea69c2f3746fff1a7e2a2) A few things it is designed for: * converting document images to Markdown * extracting structured data from documents using a target json template * handling tables, forms, and layout-heavy pages * working with both text and visual document inputs * serving as a local/open-weight alternative for document extraction pipelines 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: * [https://about.nuextract.ai/blog/nuextract-3-release](https://about.nuextract.ai/blog/nuextract-3-release) * [https://huggingface.co/numind/NuExtract3](https://huggingface.co/numind/NuExtract3) * [https://huggingface.co/collections/numind/nuextract3](https://huggingface.co/collections/numind/nuextract3) 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 [https://discord.com/invite/3tsEtJNCDe](https://discord.com/invite/3tsEtJNCDe)
Liquid AI released LFM2.5-VL-1.6B-Extract and LFM2.5-VL-450M-Extract, vision-language models that output structured JSON from images and field lists. The models are open-weight and available in two sizes.
docext is an on-premises toolkit that converts images and PDFs to markdown without OCR, leveraging vision-language models. It also introduces Nanonets-OCR-s, a compact 3B parameter model for efficient image-to-markdown conversion.
dots.ocr is a new lightweight 1.7B parameter multilingual vision-language model that achieves state-of-the-art performance on OmniDocBench, outperforming much larger models (72B+) at document parsing and OCR tasks.
Alibaba Qwen announces two major model releases: Qwen3-Omni, the first natively end-to-end omni-modal AI unifying text, image, audio and video, and Qwen3-Next-80B-A3B, an ultra-efficient MoE model with 3B activated parameters per token, achieving SOTA performance and 10x faster inference than Qwen3-32B.