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# /// script
# requires-python = ">=3.12"
# dependencies = [
# "torch",
# "torchvision",
# "transformers",
# "accelerate",
# "peft",
# "slack-sdk",
# ]
# ///
try:
# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText
processor = AutoProcessor.from_pretrained("LiquidAI/LFM2-VL-3B")
model = AutoModelForImageTextToText.from_pretrained("LiquidAI/LFM2-VL-3B")
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('LiquidAI_LFM2-VL-3B_1.txt', 'w', encoding='utf-8') as f:
f.write('Everything was good in LiquidAI_LFM2-VL-3B_1.txt')
except Exception as e:
import os
from slack_sdk import WebClient
client = WebClient(token=os.environ['SLACK_TOKEN'])
client.chat_postMessage(
channel='#exp-slack-alerts',
text='Problem in <https://huggingface.co/datasets/model-metadata/code_execution_files/blob/main/LiquidAI_LFM2-VL-3B_1.txt|LiquidAI_LFM2-VL-3B_1.txt>',
)
with open('LiquidAI_LFM2-VL-3B_1.txt', 'a', encoding='utf-8') as f:
import traceback
f.write('''```CODE:
# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText
processor = AutoProcessor.from_pretrained("LiquidAI/LFM2-VL-3B")
model = AutoModelForImageTextToText.from_pretrained("LiquidAI/LFM2-VL-3B")
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='LiquidAI_LFM2-VL-3B_1.txt',
repo_id='model-metadata/code_execution_files',
path_in_repo='LiquidAI_LFM2-VL-3B_1.txt',
repo_type='dataset',
)
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