Instructions to use TeeA/MATCHA-ViChart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TeeA/MATCHA-ViChart with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="TeeA/MATCHA-ViChart")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("TeeA/MATCHA-ViChart") model = AutoModelForImageTextToText.from_pretrained("TeeA/MATCHA-ViChart") - Notebooks
- Google Colab
- Kaggle
Upload Pix2StructForConditionalGeneration
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"is_encoder_decoder": true,
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"is_vqa":
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"model_type": "pix2struct",
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"pad_token_id": 0,
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"text_config": {
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"is_encoder_decoder": true,
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"is_vqa": false,
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"model_type": "pix2struct",
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"pad_token_id": 0,
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"text_config": {
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 1042252608
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