Instructions to use chenghao/idefics2-edgar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chenghao/idefics2-edgar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="chenghao/idefics2-edgar")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chenghao/idefics2-edgar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from chenghao/idefics2-edgar: direct link, hf CLI and curl.
- Browser
- Download file 483 Bytes
-
https://huggingface.co/chenghao/idefics2-edgar/resolve/main/processor_config.json
- Command line
-
hf download hf://chenghao/idefics2-edgar/processor_config.json
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curl -L -o processor_config.json https://huggingface.co/chenghao/idefics2-edgar/resolve/main/processor_config.json
483 Bytes
| { | |
| "chat_template": "{% for message in messages %}{{message['role'].capitalize()}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>\n{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}", | |
| "image_seq_len": 64, | |
| "processor_class": "Idefics2Processor" | |
| } | |