pt-br/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb

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   "source": [
    "# Alinhamento de Preferências com a Otimização Direta de Preferências (DPO)\n",
    "\n",
    "Este caderno o guiará pelo processo de ajuste fino de um modelo de linguagem usando a Otimização Direta de Preferências (DPO). Usaremos o modelo SmolLM2-135M-Instruct que já passou por um treinamento SFT, portanto, o qual agora é compatível com o DPO. Você também pode usar o modelo que treinou em [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
    "\n",
    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
    "     <h2 style='margin: 0;color:blue'>Exercício: Alinhamento do SmolLM2 com o DPOTrainer</h2>\n",
    "     <p>Pegue um conjunto de dados do hub do Hugging Face e alinhe um modelo a ele. </p> \n",
    "     <p><b>Níveis de Dificuldade</b></p>\n",
    "     <p>🐢 Use o conjunto de dados `trl-lib/ultrafeedback_binarized`.</p>\n",
    "     <p>🐕 Experimente o conjunto de dados `argilla/ultrafeedback-binarized-preferences.</p>\n",
    "     <p>🦁 Selecione um conjunto de dados relacionado a um caso de uso real no qual você esteja interessado ou use o modelo que você treinou em  \n",
    "        <a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">1_instruction_tuning</a>.</p>\n",
    "</div>"
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Install the requirements in Google Colab\n",
    "# !pip install transformers datasets trl huggingface_hub\n",
    "\n",
    "# Authenticate to Hugging Face\n",
    "\n",
    "from huggingface_hub import login\n",
    "\n",
    "login()\n",
    "\n",
    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importe os módulos\n"
   ]
  },
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    "id": "_zIBL8IssExG",
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   "source": [
    "import torch\n",
    "import os\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "from datasets import load_dataset\n",
    "from trl import DPOTrainer, DPOConfig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d8CvUgROUDw-"
   },
   "source": [
    "## Formate o conjunto de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MCD77GZ60DOT"
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   "source": [
    "# Load dataset\n",
    "\n",
    "# TODO: 🦁🐕 change the dataset to one of your choosing\n",
    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\", split=\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: 🐕 If your dataset is not represented as conversation lists, you can use the `process_dataset` function to convert it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecione o modelo\n",
    "\n",
    "Usaremos o modelo SmolLM2-135M-Instruct que já passou por um treinamento SFT, que agora é compatível com o DPO. Você também pode usar o modelo que treinou em [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
    "\n",
    "\n",
    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; width:80%; color:black'>\n",
    "     <p>🦁 altere o modelo para o caminho ou o ID do repositório do modelo em que você treinou o <a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">1_instruction_tuning</a>.</p>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: 🦁 change the model to the path or repo id of the model you trained in [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb)\n",
    "\n",
    "model_name = \"HuggingFaceTB/SmolLM2-135M-Instruct\"\n",
    "\n",
    "device = (\n",
    "    \"cuda\"\n",
    "    if torch.cuda.is_available()\n",
    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
    ")\n",
    "\n",
    "# Model to fine-tune\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    pretrained_model_name_or_path=model_name,\n",
    "    torch_dtype=torch.float16,\n",
    ").to(device)\n",
    "model.config.use_cache = False\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "\n",
    "# Set our name for the finetune to be saved &/ uploaded to\n",
    "finetune_name = \"SmolLM2-FT-DPO\"\n",
    "finetune_tags = [\"smol-course\", \"module_1\"]"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Treine o modelo com DPO"
   ]
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    "# Training arguments\n",
    "training_args = DPOConfig(\n",
    "    # Training batch size per GPU\n",
    "    per_device_train_batch_size=4,\n",
    "    # Number of updates steps to accumulate before performing a backward/update pass\n",
    "    # Effective batch size = per_device_train_batch_size * gradient_accumulation_steps\n",
    "    gradient_accumulation_steps=4,\n",
    "    # Saves memory by not storing activations during forward pass\n",
    "    # Instead recomputes them during backward pass\n",
    "    gradient_checkpointing=True,\n",
    "    # Base learning rate for training\n",
    "    learning_rate=5e-5,\n",
    "    # Learning rate schedule - 'cosine' gradually decreases LR following cosine curve\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "    # Total number of training steps\n",
    "    max_steps=200,\n",
    "    # Disables model checkpointing during training\n",
    "    save_strategy=\"no\",\n",
    "    # How often to log training metrics\n",
    "    logging_steps=1,\n",
    "    # Directory to save model outputs\n",
    "    output_dir=\"smol_dpo_output\",\n",
    "    # Number of steps for learning rate warmup\n",
    "    warmup_steps=100,\n",
    "    # Use bfloat16 precision for faster training\n",
    "    bf16=True,\n",
    "    # Disable wandb/tensorboard logging\n",
    "    report_to=\"none\",\n",
    "    # Keep all columns in dataset even if not used\n",
    "    remove_unused_columns=False,\n",
    "    # Enable MPS (Metal Performance Shaders) for Mac devices\n",
    "    use_mps_device=device == \"mps\",\n",
    "    # Model ID for HuggingFace Hub uploads\n",
    "    hub_model_id=finetune_name,\n",
    ")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "trainer = DPOTrainer(\n",
    "    # The model to be trained\n",
    "    model=model,\n",
    "    # Training configuration from above\n",
    "    args=training_args,\n",
    "    # Dataset containing preferred/rejected response pairs\n",
    "    train_dataset=dataset,\n",
    "    # Tokenizer for processing inputs\n",
    "    processing_class=tokenizer,\n",
    "    # DPO-specific temperature parameter that controls the strength of the preference model\n",
    "    # Lower values (like 0.1) make the model more conservative in following preferences\n",
    "    beta=0.1,\n",
    "    # Maximum length of the input prompt in tokens\n",
    "    max_prompt_length=1024,\n",
    "    # Maximum combined length of prompt + response in tokens\n",
    "    max_length=1536,\n",
    ")"
   ]
  },
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   "source": [
    "# Train the model\n",
    "trainer.train()\n",
    "\n",
    "# Save the model\n",
    "trainer.save_model(f\"./{finetune_name}\")\n",
    "\n",
    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
    "if os.getenv(\"HF_TOKEN\"):\n",
    "    trainer.push_to_hub(tags=finetune_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 💐 Você terminou!\n",
    "\n",
    "Este caderno forneceu um guia passo-a-passo para o ajuste fino do modelo `HuggingFaceTB/SmolLM2-135M` usando o `DPOTrainer`. Seguindo essas etapas, você pode adaptar o modelo para executar tarefas específicas com mais eficiência. Se quiser continuar trabalhando neste curso, aqui estão as etapas que você pode experimentar:\n",
    "\n",
    "- Experimente este caderno em uma dificuldade maior\n",
    "- Revisar o PR de um colega\n",
    "- Melhorar o material do curso por meio de uma Issue ou PR."
   ]
  }
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