AvitoTech_avision_1.py

# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "torch",
#     "torchvision",
#     "transformers",
#     "diffusers",
#     "sentence-transformers",
#     "accelerate",
#     "peft",
#     "slack-sdk",
# ]
# ///

try:
    # Load model directly
    from transformers import AutoProcessor, AutoModelForVision2Seq
    
    processor = AutoProcessor.from_pretrained("AvitoTech/avision")
    model = AutoModelForVision2Seq.from_pretrained("AvitoTech/avision")
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
                {"type": "text", "text": "What animal is on the candy?"}
            ]
        },
    ]
    inputs = processor.apply_chat_template(
    	messages,
    	add_generation_prompt=True,
    	tokenize=True,
    	return_dict=True,
    	return_tensors="pt",
    ).to(model.device)
    
    outputs = model.generate(**inputs, max_new_tokens=40)
    print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
    with open('AvitoTech_avision_1.txt', 'w', encoding='utf-8') as f:
        f.write('Everything was good in AvitoTech_avision_1.txt')
except Exception as e:
    import os
    from slack_sdk import WebClient
    client = WebClient(token=os.environ['SLACK_TOKEN'])
    client.chat_postMessage(
        channel='#hub-model-metadata-snippets-sprint',
        text='Problem in <https://huggingface.co/datasets/model-metadata/code_execution_files/blob/main/AvitoTech_avision_1.txt|AvitoTech_avision_1.txt>',
    )

    with open('AvitoTech_avision_1.txt', 'a', encoding='utf-8') as f:
        import traceback
        f.write('''```CODE: 
# Load model directly
from transformers import AutoProcessor, AutoModelForVision2Seq

processor = AutoProcessor.from_pretrained("AvitoTech/avision")
model = AutoModelForVision2Seq.from_pretrained("AvitoTech/avision")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
```

ERROR: 
''')
        traceback.print_exc(file=f)
    
finally:
    from huggingface_hub import upload_file
    upload_file(
        path_or_fileobj='AvitoTech_avision_1.txt',
        repo_id='model-metadata/code_execution_files',
        path_in_repo='AvitoTech_avision_1.txt',
        repo_type='dataset',
    )