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# /// script
# requires-python = ">=3.12"
# dependencies = [
# "numpy",
# "einops",
# "torch",
# "transformers",
# "diffusers",
# "datasets",
# "accelerate",
# "timm",
# ]
# ///
try:
from huggingface_hub import login
login(new_session=False)
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="CohereLabs/command-a-vision-07-2025")
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?"}
]
},
]
pipe(text=messages)
# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText
processor = AutoProcessor.from_pretrained("CohereLabs/command-a-vision-07-2025")
model = AutoModelForImageTextToText.from_pretrained("CohereLabs/command-a-vision-07-2025")
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('CohereLabs_command-a-vision-07-2025_0.txt', 'w') as f:
f.write('Everything was good in CohereLabs_command-a-vision-07-2025_0.txt')
except Exception as e:
with open('CohereLabs_command-a-vision-07-2025_0.txt', 'w') as f:
import traceback
traceback.print_exc(file=f)
finally:
from huggingface_hub import upload_file
upload_file(
path_or_fileobj='CohereLabs_command-a-vision-07-2025_0.txt',
repo_id='model-metadata/custom_code_execution_files',
path_in_repo='CohereLabs_command-a-vision-07-2025_0.txt',
repo_type='dataset',
) |