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# /// script
# requires-python = ">=3.12"
# dependencies = [
# "torch",
# "torchvision",
# "transformers",
# "diffusers",
# "sentence-transformers",
# "accelerate",
# "peft",
# "slack-sdk",
# ]
# ///
try:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "HuggingFaceTB/SmolLM3-3B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
).to(device)
with open('HuggingFaceTB_SmolLM3-3B_3.txt', 'w', encoding='utf-8') as f:
f.write('Everything was good in HuggingFaceTB_SmolLM3-3B_3.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/HuggingFaceTB_SmolLM3-3B_3.txt|HuggingFaceTB_SmolLM3-3B_3.txt>',
)
with open('HuggingFaceTB_SmolLM3-3B_3.txt', 'a', encoding='utf-8') as f:
import traceback
f.write('''```CODE:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "HuggingFaceTB/SmolLM3-3B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
).to(device)
```
ERROR:
''')
traceback.print_exc(file=f)
finally:
from huggingface_hub import upload_file
upload_file(
path_or_fileobj='HuggingFaceTB_SmolLM3-3B_3.txt',
repo_id='model-metadata/code_execution_files',
path_in_repo='HuggingFaceTB_SmolLM3-3B_3.txt',
repo_type='dataset',
)
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