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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README.md
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license: mit
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base_model: Qwen/Qwen2.5-VL-7B
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tags:
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model_name: NuMarkdown-8B-Thinking
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library_name: transformers
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pipeline_tag:
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---
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<p align="center">
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reasoning = result.split("<think>")[1].split("</think>")[0]
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answer = result.split("<answer>")[1].split("</answer>")[0]
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print(answer)
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```
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license: mit
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base_model: Qwen/Qwen2.5-VL-7B
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tags:
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- OCR
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- vision-language
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- VLM
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- Reasoning
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- document-to-markdown
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- qwen2.5
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- markdown
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- extraction
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- RAG
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model_name: NuMarkdown-8B-Thinking
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library_name: transformers
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pipeline_tag: image-to-text
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---
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<p align="center">
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reasoning = result.split("<think>")[1].split("</think>")[0]
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answer = result.split("<answer>")[1].split("</answer>")[0]
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print(answer)
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```
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