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, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("numind/NuMarkdown-8B-Thinking") model = AutoModelForImageTextToText.from_pretrained("numind/NuMarkdown-8B-Thinking") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- ba17b771fcbfd2dd9a0cc4012eced44633654a17c4bf42e8e926dd365e7bbbf7
- Size of remote file:
- 163 kB
- SHA256:
- 9ab65794a94eae69f761e65fa4731829251b907b60488bab62b47b5c16cc7000
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.