Image-to-Text
Transformers
Safetensors
qwen2_5_vl
image-text-to-text
vision-language-model
table-extraction
financial-documents
qwen2.5-vl
fine-tuned
text-generation-inference
Instructions to use Glazkov/qwen2.5-vl-table-extraction-ru-v0.1-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Glazkov/qwen2.5-vl-table-extraction-ru-v0.1-bnb-4bit 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="Glazkov/qwen2.5-vl-table-extraction-ru-v0.1-bnb-4bit")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Glazkov/qwen2.5-vl-table-extraction-ru-v0.1-bnb-4bit") model = AutoModelForMultimodalLM.from_pretrained("Glazkov/qwen2.5-vl-table-extraction-ru-v0.1-bnb-4bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bd2f092eeec244c0448f13e31edd4b25e625568713e4155993a3dea90c7437a
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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