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try: |
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from transformers import AutoProcessor, AutoModelForVision2Seq |
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processor = AutoProcessor.from_pretrained("ibm-granite/granite-docling-258M") |
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model = AutoModelForVision2Seq.from_pretrained("ibm-granite/granite-docling-258M") |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, |
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{"type": "text", "text": "What animal is on the candy?"} |
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] |
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}, |
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] |
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inputs = processor.apply_chat_template( |
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messages, |
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add_generation_prompt=True, |
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tokenize=True, |
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return_dict=True, |
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return_tensors="pt", |
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).to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=40) |
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print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) |
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with open('ibm-granite_granite-docling-258M_1.txt', 'w', encoding='utf-8') as f: |
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f.write('Everything was good in ibm-granite_granite-docling-258M_1.txt') |
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except Exception as e: |
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with open('ibm-granite_granite-docling-258M_1.txt', 'w', encoding='utf-8') as f: |
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import traceback |
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traceback.print_exc(file=f) |
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finally: |
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from huggingface_hub import upload_file |
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upload_file( |
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path_or_fileobj='ibm-granite_granite-docling-258M_1.txt', |
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repo_id='model-metadata/code_execution_files', |
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path_in_repo='ibm-granite_granite-docling-258M_1.txt', |
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repo_type='dataset', |
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) |
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