Spaces:
Running
Running
File size: 4,239 Bytes
e77bff1 e205424 e77bff1 e205424 e77bff1 e205424 e77bff1 e205424 e77bff1 7e93b98 e77bff1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
{
"notebook_title": "Supervised fine-tuning (SFT)",
"notebook_type": "sft",
"dataset_types": ["text"],
"compatible_library": "datasets",
"notebook_template": [
{
"cell_type": "markdown",
"source": "---\n# **Supervised fine-tuning Notebook for {dataset_name} dataset**\n---"
},
{
"cell_type": "markdown",
"source": "## 1. Setup necessary libraries and load the dataset"
},
{
"cell_type": "code",
"source": "# Install and import necessary libraries\n!pip install trl datasets transformers bitsandbytes"
},
{
"cell_type": "code",
"source": "from datasets import load_dataset\nfrom trl import SFTTrainer\nfrom transformers import TrainingArguments"
},
{
"cell_type": "code",
"source": "# Load the dataset\ndataset = load_dataset('{dataset_name}', name='{first_config}', split='{first_split}')\ndataset"
},
{
"cell_type": "code",
"source": "# Split the dataset: 20% for evaluation, 80% for training\ntrain_test_split = dataset.train_test_split(test_size=0.2)\n\n# Get the training and evaluation datasets\ntrain_dataset = train_test_split['train']\neval_dataset = train_test_split['test']"
},
{
"cell_type": "code",
"source": "# Specify the column name that will be used for training\ndataset_text_field = '{longest_col}'"
},
{
"cell_type": "markdown",
"source": "## 2. Configure SFT trainer"
},
{
"cell_type": "code",
"source": "model_name = 'facebook/opt-350m' # Replace with your desired model\noutput_model_name = f'{model_name}-{dataset_name}'.replace('/', '-')"
},
{
"cell_type": "code",
"source": "# Initialize training arguments, adjust parameters as needed\ntraining_args = TrainingArguments(\n per_device_train_batch_size = 1, #Batch size per GPU for training\n gradient_accumulation_steps = 4,\n max_steps = 100, #Total number of training steps.(Overrides epochs)\n learning_rate = 2e-4,\n fp16 = True,\n logging_steps=20,\n output_dir = output_model_name,\n optim = 'paged_adamw_8bit' #Optimizer to use\n )"
},
{
"cell_type": "code",
"source": "# Initialize SFTTrainer\ntrainer = SFTTrainer(\n model = model_name,\n train_dataset=train_dataset,\n eval_dataset=eval_dataset,\n dataset_text_field=dataset_text_field,\n max_seq_length=512,\n args=training_args\n)"
},
{
"cell_type": "markdown",
"source": "## 3. Perform fine-tuning and capture the training process"
},
{
"cell_type": "code",
"source": "eval_result_before = trainer.evaluate()\n\n# Start training\ntrainer.train()\n\neval_result_after = trainer.evaluate()"
},
{
"cell_type": "code",
"source": "print(f'Before training: {eval_result_before}')\nprint(f'After training: {eval_result_after}')"
},
{
"cell_type": "markdown",
"source": "## 4. Compare model output vs original"
},
{
"cell_type": "code",
"source": "from transformers import pipeline, AutoTokenizer\n\nmy_model = trainer.model\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\nmy_pipe = pipeline('text-generation', model=my_model, tokenizer=tokenizer)\noriginal_pipe = pipeline('text-generation', model=model_name)"
},
{
"cell_type": "code",
"source": "print(original_pipe('Hello'))\nprint(my_pipe('Hello'))"
},
{
"cell_type": "markdown",
"source": "## 5. Push model to hub (Optional)"
},
{
"cell_type": "code",
"source": "# Authenticate to the Hugging Face Hub\nfrom huggingface_hub import notebook_login\nnotebook_login()"
},
{
"cell_type": "code",
"source": "# Push the model to Hugging Face Hub\ntrainer.push_to_hub()"
}
]
} |