🐛 fix training cell: manual ChatML format, list return for Unsloth SFTTrainer

notebooks/qwen35-2b-bash-finetune.ipynb

@@ -174,41 +174,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from trl import SFTTrainer, SFTConfig\n",
-    "\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    tokenizer=tokenizer,\n",
-    "    train_dataset=train_dataset,\n",
-    "    eval_dataset=val_dataset,\n",
-    "    args=SFTConfig(\n",
-    "        max_seq_length=max_seq_length,\n",
-    "        per_device_train_batch_size=4,\n",
-    "        gradient_accumulation_steps=4,\n",
-    "        warmup_steps=20,\n",
-    "        num_train_epochs=3,\n",
-    "        logging_steps=25,\n",
-    "        eval_strategy=\"steps\",\n",
-    "        eval_steps=200,\n",
-    "        save_steps=200,\n",
-    "        save_total_limit=3,\n",
-    "        load_best_model_at_end=True,\n",
-    "        metric_for_best_model=\"eval_loss\",\n",
-    "        learning_rate=1e-4,\n",
-    "        optim=\"adamw_8bit\",\n",
-    "        weight_decay=0.01,\n",
-    "        lr_scheduler_type=\"cosine\",\n",
-    "        seed=3407,\n",
-    "        output_dir=\"outputs_qwen35_2b\",\n",
-    "        dataset_text_field=\"text\",\n",
-    "    ),\n",
-    ")\n",
-    "\n",
-    "trainer_stats = trainer.train()\n",
-    "print(f\"\\nTraining time: {trainer_stats.metrics['train_runtime']:.0f}s\")\n",
-    "print(f\"Peak memory: {torch.cuda.max_memory_allocated() / 1e9:.2f} GB\")"
-   ]
+   "source": "from trl import SFTTrainer, SFTConfig\n\ndef format_messages(example):\n    msgs = example[\"messages\"]\n    parts = []\n    for msg in msgs:\n        if isinstance(msg, dict):\n            role, content = msg[\"role\"], msg[\"content\"]\n        else:\n            role, content = msg[0], msg[1]\n        parts.append(f\"<|im_start|>{role}\\n{content}<|im_end|>\")\n    return [\"\\n\".join(parts)]\n\ntrainer = SFTTrainer(\n    model=model,\n    tokenizer=tokenizer,\n    train_dataset=train_dataset,\n    eval_dataset=val_dataset,\n    formatting_func=format_messages,\n    args=SFTConfig(\n        max_seq_length=max_seq_length,\n        per_device_train_batch_size=4,\n        gradient_accumulation_steps=4,\n        warmup_steps=20,\n        num_train_epochs=3,\n        logging_steps=25,\n        eval_strategy=\"steps\",\n        eval_steps=200,\n        save_steps=200,\n        save_total_limit=3,\n        load_best_model_at_end=True,\n        metric_for_best_model=\"eval_loss\",\n        learning_rate=1e-4,\n        optim=\"adamw_8bit\",\n        weight_decay=0.01,\n        lr_scheduler_type=\"cosine\",\n        seed=3407,\n        output_dir=\"outputs_qwen35_2b\",\n    ),\n)\n\ntrainer_stats = trainer.train()\nprint(f\"\\nTraining time: {trainer_stats.metrics['train_runtime']:.0f}s\")\nprint(f\"Peak memory: {torch.cuda.max_memory_allocated() / 1e9:.2f} GB\")"
   },
   {
    "cell_type": "markdown",