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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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import argparse |
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def get_args(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--path", type=str) |
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parser.add_argument("--hub-name", type=str) |
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return parser.parse_args() |
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def main(): |
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args = get_args() |
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print(f"Args: {args}") |
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print(f"Loading tokenizer from path: {args.path}") |
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tokenizer = AutoTokenizer.from_pretrained(args.path) |
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print(f"Pushing the tokenizer to the Hub at {args.hub_name}") |
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tokenizer.push_to_hub(args.hub_name, private=True) |
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print(f"Loading model from path: {args.path}") |
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model = AutoModelForCausalLM.from_pretrained( |
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args.path, |
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return_dict=True, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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) |
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print(f"Pushing the model to the Hub at {args.hub_name}") |
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model.push_to_hub(args.hub_name, private=True) |
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from huggingface_hub import HfApi |
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api = HfApi() |
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for file in ["training_args.bin", "all_results.json", "eval_results.json"]: |
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try: |
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api.upload_file( |
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path_or_fileobj=f"{args.path}/{file}", |
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path_in_repo=file, |
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repo_id=args.hub_name, |
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repo_type="model", |
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) |
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except Exception as e: |
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print(f"Failed to upload {file}: {e}") |
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if __name__ == "__main__": |
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main() |
