Image-to-Text
Transformers
Safetensors
qwen2_5_vl
image-text-to-text
OCR
vision-language
VLM
Reasoning
document-to-markdown
qwen2.5
markdown
extraction
RAG
text-generation-inference
Instructions to use numind/NuMarkdown-8B-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use numind/NuMarkdown-8B-Thinking 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/NuMarkdown-8B-Thinking")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("numind/NuMarkdown-8B-Thinking") model = AutoModelForMultimodalLM.from_pretrained("numind/NuMarkdown-8B-Thinking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# NuMarkdown-reasoning 📄
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**NuMarkdown-reasoning** is the first reasoning vision-language model trained to
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It is a fine-tune of **Qwen 2.5-VL-7B** using ~10 k synthetic doc-to-Reasoning-to-Markdown pairs, followed by a RL phase (GRPO) with a layout-centric reward.
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*(note: the number of thinking tokens can vary from 20% to 2X the number of token of the final answers)*
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# NuMarkdown-reasoning 📄
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**NuMarkdown-reasoning** is the first reasoning vision-language model trained specifically to convert documents into clean GitHub-flavoured Markdown.
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It is a fine-tune of **Qwen 2.5-VL-7B** using ~10 k synthetic doc-to-Reasoning-to-Markdown pairs, followed by a RL phase (GRPO) with a layout-centric reward.
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*(note: the number of thinking tokens can vary from 20% to 2X the number of token of the final answers)*
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