HuggingFaceTB_SmolLM3-3B_3.py

# /// 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',
    )