Instructions to use google/matcha-chart2text-pew with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/matcha-chart2text-pew with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "visual-question-answering" 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("visual-question-answering", model="google/matcha-chart2text-pew")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("google/matcha-chart2text-pew") model = AutoModelForMultimodalLM.from_pretrained("google/matcha-chart2text-pew", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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processor.push_to_hub("USERNAME/MODEL_NAME")
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# Contribution
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This model was originally contributed by Fangyu Liu, Francesco Piccinno et al. and added to the Hugging Face ecosystem by [Younes Belkada](https://huggingface.co/ybelkada).
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processor.push_to_hub("USERNAME/MODEL_NAME")
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```
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## Run predictions
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To run predictions, refer to the [instructions presented in the `matcha-chartqa` model card](https://huggingface.co/ybelkada/matcha-chartqa#get-predictions-from-the-model).
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# Contribution
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This model was originally contributed by Fangyu Liu, Francesco Piccinno et al. and added to the Hugging Face ecosystem by [Younes Belkada](https://huggingface.co/ybelkada).
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