|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
try: |
|
|
|
|
|
from transformers import AutoProcessor, AutoModelForVision2Seq |
|
|
|
|
|
processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-32B-Instruct-FP8") |
|
|
model = AutoModelForVision2Seq.from_pretrained("Qwen/Qwen3-VL-32B-Instruct-FP8") |
|
|
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('Qwen_Qwen3-VL-32B-Instruct-FP8_1.txt', 'w', encoding='utf-8') as f: |
|
|
f.write('Everything was good in Qwen_Qwen3-VL-32B-Instruct-FP8_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/Qwen_Qwen3-VL-32B-Instruct-FP8_1.txt|Qwen_Qwen3-VL-32B-Instruct-FP8_1.txt>', |
|
|
) |
|
|
|
|
|
with open('Qwen_Qwen3-VL-32B-Instruct-FP8_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("Qwen/Qwen3-VL-32B-Instruct-FP8") |
|
|
model = AutoModelForVision2Seq.from_pretrained("Qwen/Qwen3-VL-32B-Instruct-FP8") |
|
|
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='Qwen_Qwen3-VL-32B-Instruct-FP8_1.txt', |
|
|
repo_id='model-metadata/code_execution_files', |
|
|
path_in_repo='Qwen_Qwen3-VL-32B-Instruct-FP8_1.txt', |
|
|
repo_type='dataset', |
|
|
) |
|
|
|
