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@@ -237,7 +237,8 @@ def main(**kwargs):
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if not train_config.use_peft and train_config.freeze_LLM_only and config.model_type == "mllama":
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freeze_LLM_only(model)
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-
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+
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+ print_model_size(model, train_config, rank if train_config.enable_fsdp else 0)
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mixed_precision_policy, wrapping_policy = get_policies(fsdp_config, rank)
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# Create the FSDP wrapper for MllamaSelfAttentionDecoderLayer,MllamaSelfAttentionDecoderLayer,MllamaVisionEncoderLayer in vision models
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@@ -306,8 +307,6 @@ def main(**kwargs):
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dataset_processer = processor
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else:
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dataset_processer = tokenizer
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-
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- print_model_size(model, train_config, rank if train_config.enable_fsdp else 0)
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# Load and preprocess the dataset for training and validation
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