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| import argparse | |
| import torch | |
| from transformers import ( | |
| AutoProcessor, | |
| LlavaForConditionalGeneration, | |
| ) | |
| def preprocess_text_encoder_tokenizer(args): | |
| processor = AutoProcessor.from_pretrained(args.input_dir) | |
| model = LlavaForConditionalGeneration.from_pretrained( | |
| args.input_dir, | |
| torch_dtype=torch.float16, | |
| low_cpu_mem_usage=True, | |
| ).to(0) | |
| model.language_model.save_pretrained( | |
| f"{args.output_dir}" | |
| ) | |
| processor.tokenizer.save_pretrained( | |
| f"{args.output_dir}" | |
| ) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "--input_dir", | |
| type=str, | |
| required=True, | |
| help="The path to the llava-llama-3-8b-v1_1-transformers.", | |
| ) | |
| parser.add_argument( | |
| "--output_dir", | |
| type=str, | |
| default="", | |
| help="The output path of the llava-llama-3-8b-text-encoder-tokenizer." | |
| "if '', the parent dir of output will be the same as input dir.", | |
| ) | |
| args = parser.parse_args() | |
| if len(args.output_dir) == 0: | |
| args.output_dir = "/".join(args.input_dir.split("/")[:-1]) | |
| preprocess_text_encoder_tokenizer(args) | |