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")# 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
Update README.md
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README.md
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@@ -36,7 +36,6 @@ It is a fine-tune of **Qwen 2.5-VL-7B** using ~10 k synthetic doc-to-Reasoning-t
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*we plan to realease a markdown arena -similar to llmArena- for complex document to markdown task*
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### Arena ranking (using trueskill-2 ranking system)
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| Rank | Model | μ | σ | μ − 3σ |
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| 🥇 1 | **gemini-flash-reasoning** | 26.75 | 0.80 | 24.35 |
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*we plan to realease a markdown arena -similar to llmArena- for complex document to markdown task*
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### Arena ranking (using trueskill-2 ranking system)
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| Rank | Model | μ | σ | μ − 3σ |
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| ---- | --------------------------------------- | ----- | ---- | ------ |
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| 🥇 1 | **gemini-flash-reasoning** | 26.75 | 0.80 | 24.35 |
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