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
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## Training
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1. **SFT**: One-epoch supervised fine-tune on synthetic reasoning trace generated from public PDFs (10K input/output pairs).
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2. **RL (GRPO)**: RL phase using a
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**Model before GRPO loose 80% time vs post GRPO model (see win-rate matrix)**
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## Training
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1. **SFT**: One-epoch supervised fine-tune on synthetic reasoning trace generated from public PDFs (10K input/output pairs).
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2. **RL (GRPO)**: RL phase using a layout-centric reward (5K difficults image examples).
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**Model before GRPO loose 80% time vs post GRPO model (see win-rate matrix)**
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