Add learning rate scheduler and early stopping to FineTune LLMs

[lucas-aixplain]

Task: M-5542693469

[thiago-aixplain]

Does @dataclass_json converts the enumerator to string automatically?

[lucas-aixplain]

yes, by running the following line:

finetune = FinetuneFactory.create(str(uuid.uuid4()), dataset_list, model, hyperparameters=Hyperparameters(lr_scheduler_type=SchedulerType.CONSTANT))

We get the following request body:

{
  "datasets": [
    {
      "datasetId": "65293795abed4d551b86baac",
      "trainPercentage": 100,
      "devPercentage": 0
    }
  ],
  "sourceModelId": "64e615671567f848804985e1",
  "parameters": {
    "hyperparameters": {
      "epochs": 4,
      "train_batch_size": 4,
      "eval_batch_size": 4,
      "learning_rate": 2e-05,
      "generation_max_length": 225,
      "tokenizer_batch_size": 256,
      "gradient_checkpointing": false,
      "gradient_accumulation_steps": 1,
      "max_seq_length": 4096,
      "warmup_ratio": 0.0,
      "warmup_steps": 0,
      "early_stopping_patience": 1,
      "lr_scheduler_type": "constant"
    }
  }
}

[thiago-aixplain]

should you include these parameters in the functional test?