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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("TeeA/MATCHA-ViChart") model = AutoModelForMultimodalLM.from_pretrained("TeeA/MATCHA-ViChart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 775 Bytes
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"_name_or_path": "TeeA/MATCHA-ViChart",
"architectures": [
"Pix2StructForConditionalGeneration"
],
"decoder_start_token_id": 0,
"eos_token_id": 1,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"is_encoder_decoder": true,
"is_vqa": false,
"model_type": "pix2struct",
"pad_token_id": 0,
"text_config": {
"encoder_hidden_size": 768,
"initializer_range": 0.02,
"model_type": "pix2struct_text_model",
"vocab_size": 36096
},
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.35.2",
"vision_config": {
"initializer_range": 0.02,
"layer_norm_bias": false,
"model_type": "pix2struct_vision_model",
"num_channels": 3,
"patch_size": 16,
"projection_dim": 768
}
}
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