pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb (8,643 lines of code) (raw):
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Alinhamento de Preferências com Otimização de Preferências de Razão de Chances (ORPO)\n",
"\n",
"Este caderno o guiará pelo processo de ajuste fino de um modelo de linguagem usando a Otimização de Preferências de Razão de Chances (ORPO). Usaremos o modelo SmolLM2-135M que **não** passou pelo treinamento do SFT, portanto, não é compatível com o DPO. Isso significa que você não 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>π’ Use o conjunto de dados `trl-lib/ultrafeedback_binarized`.</p>\n",
" <p>π Experimente usar o conjunto de dados `argilla/ultrafeedback-binarized-preferences.</p>\n",
" <p>π¦ Experimente usar um subconjunto do conjunto de dados `orpo-dpo-mix-40k` de mlabonne.</p>\n",
"</div>\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importe os módulos\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EwNxh0pCQhfI",
"outputId": "e4c288e1-2fa3-4717-d9dd-ae3ec0b91e3b"
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"text": [
"/Users/ben/code/smol-course/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import torch\n",
"import os\n",
"from datasets import load_dataset\n",
"from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
")\n",
"from trl import ORPOConfig, ORPOTrainer, setup_chat_format\n",
"\n",
"# Authenticate to Hugging Face\n",
"from huggingface_hub import login\n",
"\n",
"login()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Formate o conjunto de dados"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Generating train split: 100%|ββββββββββ| 62135/62135 [00:00<00:00, 450465.27 examples/s]\n",
"Generating test split: 100%|ββββββββββ| 1000/1000 [00:00<00:00, 382866.64 examples/s]\n"
]
}
],
"source": [
"# Load dataset\n",
"\n",
"# TODO: π¦π change the dataset to one of your choosing\n",
"dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: π If your dataset is not represented as conversation lists, you can use the `process_dataset` function to convert it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defina o modelo"
]
},
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"model_name = \"HuggingFaceTB/SmolLM2-135M\"\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",
"model, tokenizer = setup_chat_format(model, tokenizer)\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\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Treine o modelo com ORPO"
]
},
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"orpo_args = ORPOConfig(\n",
" # Small learning rate to prevent catastrophic forgetting\n",
" learning_rate=8e-6,\n",
" # Linear learning rate decay over training\n",
" lr_scheduler_type=\"linear\",\n",
" # Maximum combined length of prompt + completion\n",
" max_length=1024,\n",
" # Maximum length for input prompts\n",
" max_prompt_length=512,\n",
" # Controls weight of the odds ratio loss (λ in paper)\n",
" beta=0.1,\n",
" # Batch size for training\n",
" per_device_train_batch_size=2,\n",
" per_device_eval_batch_size=2,\n",
" # Helps with training stability by accumulating gradients before updating\n",
" gradient_accumulation_steps=4,\n",
" # Memory-efficient optimizer for CUDA, falls back to adamw_torch for CPU/MPS\n",
" optim=\"paged_adamw_8bit\" if device == \"cuda\" else \"adamw_torch\",\n",
" # Number of training epochs\n",
" num_train_epochs=1,\n",
" # When to run evaluation\n",
" evaluation_strategy=\"steps\",\n",
" # Evaluate every 20% of training\n",
" eval_steps=0.2,\n",
" # Log metrics every step\n",
" logging_steps=1,\n",
" # Gradual learning rate warmup\n",
" warmup_steps=10,\n",
" # Disable external logging\n",
" report_to=\"none\",\n",
" # Where to save model/checkpoints\n",
" output_dir=\"./results/\",\n",
" # Enable MPS (Metal Performance Shaders) if available\n",
" use_mps_device=device == \"mps\",\n",
" hub_model_id=finetune_name,\n",
")"
]
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"trainer = ORPOTrainer(\n",
" model=model,\n",
" args=orpo_args,\n",
" train_dataset=dataset[\"train\"],\n",
" eval_dataset=dataset[\"test\"],\n",
" processing_class=tokenizer,\n",
")"
]
},
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"trainer.train() # Train the model\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": [
"## π Você terminou!\n",
"\n",
"Este caderno forneceu um guia passo-a-passo para o ajuste fino do modelo `HuggingFaceTB/SmolLM2-135M` usando o `ORPOTrainer`. 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 notebook 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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