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model_saving
infrastructure-model-saving-when-using-hugging-face-trainer-to-fine-tune-a-gem-691d47b1
IFWhen using Hugging Face Trainer to fine-tune a Gemma 2 (or Gemma 3) model, saving in safetensors format fails with RuntimeError about shared tensors (e.g., embed_tokens.weight and lm_head.weight).
THENSet `save_safetensors=False` in your TrainingArguments to fall back to PyTorch serialization, or use `model.save_pretrained(safe_serialization=False)` directly. This avoids the safetensors shared-tensor check.
Tier 170%
model_saving
infrastructure-model-saving-runtimeerror-some-tensors-share-memory-when-saving-73d39cba
IFRuntimeError: Some tensors share memory when saving a model with safetensors, e.g., during fine-tuning with Trainer.
THENWhen saving a fine-tuned model that has shared tensors (common in models like Gemma 2 where embedding and lm_head weigh the same tensor), either use the `save_model` method instead of the default safetensors serialization, or set `save_safetensors=False` in TrainingArguments to fall back to PyTorch's native saving format. Ensure the workaround does not affect inference or downstream use.
Tier 170%
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