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model_saving_shared_tensors
ai-agents-model-saving-shared--when-fine-tuning-a-model-with-shared-tensor-weight-0aa34389
IFWhen fine-tuning a model with shared tensor weights (e.g., embed_tokens and lm_head) using the Trainer class, saving in safetensors format fails with a RuntimeError about shared memory.
THENDisable safe_serialization by setting save_safetensors=False in TrainingArguments, or manually save using model.save_pretrained with safe_serialization=False. Alternatively, ensure that shared tensors are handled by removing sharing before saving, or use the save_model method instead. The root cause is that Trainer's default save logic for safetensors does not correctly detect and handle shared tensors in models like Gemma 2.
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