Image-to-Text
Transformers
Safetensors
qwen3_5
image-text-to-text
vision-language
vlm
document-understanding
structured-extraction
information-extraction
ocr
document-to-markdown
markdown
rag
reasoning
multilingual
conversational
Instructions to use numind/NuExtract3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use numind/NuExtract3 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="numind/NuExtract3") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("numind/NuExtract3") model = AutoModelForMultimodalLM.from_pretrained("numind/NuExtract3") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
chore: correct vllm serve and hf page
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by Restodecoca - opened
README.md
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It combines strong **structured information extraction** with high-quality **image-to-Markdown** conversion, making it suitable for extraction pipelines, OCR, and RAG preprocessing for all types of documents such as scans, receipts, forms, invoices, contracts or tables.
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Try it out in [the 🤗 space!](https://huggingface.co/spaces/numind/
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## Overview
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If you encounter memory issues, reduce the maximum model length and the maximum number of images:
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```bash
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vllm serve numind/
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--trust-remote-code \
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--limit-mm-per-prompt '{"image": 6, "video": 0}' \
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--chat-template-content-format openai \
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It combines strong **structured information extraction** with high-quality **image-to-Markdown** conversion, making it suitable for extraction pipelines, OCR, and RAG preprocessing for all types of documents such as scans, receipts, forms, invoices, contracts or tables.
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Try it out in [the 🤗 space!](https://huggingface.co/spaces/numind/NuExtract3)
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## Overview
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If you encounter memory issues, reduce the maximum model length and the maximum number of images:
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```bash
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vllm serve numind/NuExtract3 \
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--trust-remote-code \
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--limit-mm-per-prompt '{"image": 6, "video": 0}' \
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--chat-template-content-format openai \
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