model-metadata/code_python_files

1# /// script2# requires-python = ">=3.12"3# dependencies = [4#     "torch",5#     "torchvision",6#     "transformers",7#     "diffusers",8#     "sentence-transformers",9#     "accelerate",10#     "peft",11#     "slack-sdk",12# ]13# ///14 15try:16    import torch17    from sentence_transformers import SentenceTransformer18    19    device = "cuda" if torch.cuda.is_available() else "cpu"20    21    model_id = "google/embeddinggemma-300M"22    model = SentenceTransformer(model_id).to(device=device)23    24    print(f"Device: {model.device}")25    print(model)26    print("Total number of parameters in the model:", sum([p.numel() for _, p in model.named_parameters()]))27    with open('google_embeddinggemma-300m_2.txt', 'w', encoding='utf-8') as f:28        f.write('Everything was good in google_embeddinggemma-300m_2.txt')29except Exception as e:30    import os31    from slack_sdk import WebClient32    client = WebClient(token=os.environ['SLACK_TOKEN'])33    client.chat_postMessage(34        channel='#hub-model-metadata-snippets-sprint',35        text='Problem in <https://huggingface.co/datasets/model-metadata/code_execution_files/blob/main/google_embeddinggemma-300m_2.txt|google_embeddinggemma-300m_2.txt>',36    )37 38    with open('google_embeddinggemma-300m_2.txt', 'a', encoding='utf-8') as f:39        import traceback40        f.write('''```CODE: 41import torch42from sentence_transformers import SentenceTransformer43 44device = "cuda" if torch.cuda.is_available() else "cpu"45 46model_id = "google/embeddinggemma-300M"47model = SentenceTransformer(model_id).to(device=device)48 49print(f"Device: {model.device}")50print(model)51print("Total number of parameters in the model:", sum([p.numel() for _, p in model.named_parameters()]))52```53 54ERROR: 55''')56        traceback.print_exc(file=f)57    58finally:59    from huggingface_hub import upload_file60    upload_file(61        path_or_fileobj='google_embeddinggemma-300m_2.txt',62        repo_id='model-metadata/code_execution_files',63        path_in_repo='google_embeddinggemma-300m_2.txt',64        repo_type='dataset',65    )66