Image-to-Text
Transformers
Safetensors
image-text-to-text
qwen3_5
vision-language
vlm
document-understanding
structured-extraction
information-extraction
ocr
document-to-markdown
markdown
rag
reasoning
multilingual
conversational
Instructions to use numind/NuExtract3-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use numind/NuExtract3-FP8 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/NuExtract3-FP8") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("numind/NuExtract3-FP8", dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- b8ae26bcc2d3560cf48f5603117021988cdd0dffdbe2a5317f4882ecc8f1c3f8
- Size of remote file:
- 257 kB
- SHA256:
- 873703147b4a8cc8195f9762399b9f1fc9f9e017ff3e73c702d420cebd3b6be3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.