Add callback to save to local

examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb

@@ -1,7 +1,6 @@
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@@ -10,7 +9,6 @@
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@@ -22,7 +20,6 @@
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@@ -72,7 +69,6 @@
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@@ -139,7 +135,6 @@
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@@ -1205,7 +1200,6 @@
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@@ -1300,6 +1294,13 @@
    "source": [
     "from transformers import Seq2SeqTrainer\n",
     "\n",
+    "# Save model to local\n",
+    "class PeftSavingCallback(TrainerCallback):\n",
+    "    def on_train_end(self, args, state, control, **kwargs):\n",
+    "        kwargs[\"model\"].save_pretrained(state.best_model_checkpoint)\n",
+    "        pytorch_model_path = os.path.join(state.best_model_checkpoint, \"pytorch_model.bin\")\n",
+    "        os.remove(pytorch_model_path) if os.path.exists(pytorch_model_path) else None\n",
+    "\n",
     "trainer = Seq2SeqTrainer(\n",
     "    args=training_args,\n",
     "    model=model,\n",
@@ -1308,6 +1309,7 @@
     "    data_collator=data_collator,\n",
     "    # compute_metrics=compute_metrics,\n",
     "    tokenizer=processor.feature_extractor,\n",
+    "    callbacks=[PeftSavingCallback]\n",
     ")\n",
     "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!"
    ]
@@ -1585,7 +1587,6 @@
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    "metadata": {
@@ -1928,7 +1929,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.6"
   },
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