Instructions to use google/matcha-chartqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/matcha-chartqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="google/matcha-chartqa")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("google/matcha-chartqa") model = AutoModelForMultimodalLM.from_pretrained("google/matcha-chartqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from google/matcha-chartqa: direct link, hf CLI and curl.
- Browser
- Download file 249 Bytes
-
https://huggingface.co/google/matcha-chartqa/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://google/matcha-chartqa/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/google/matcha-chartqa/resolve/main/preprocessor_config.json
249 Bytes
| { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "image_processor_type": "Pix2StructImageProcessor", | |
| "is_vqa": true, | |
| "max_patches": 2048, | |
| "patch_size": { | |
| "height": 16, | |
| "width": 16 | |
| }, | |
| "processor_class": "Pix2StructProcessor" | |
| } | |