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| from typing import Optional | |
| from pydantic import Field | |
| from autotrain.trainers.common import AutoTrainParams | |
| class VLMTrainingParams(AutoTrainParams): | |
| """ | |
| VLMTrainingParams | |
| Attributes: | |
| model (str): Model name. Default is "google/paligemma-3b-pt-224". | |
| project_name (str): Output directory. Default is "project-name". | |
| data_path (str): Data path. Default is "data". | |
| train_split (str): Train data config. Default is "train". | |
| valid_split (Optional[str]): Validation data config. Default is None. | |
| trainer (str): Trainer type (captioning, vqa, segmentation, detection). Default is "vqa". | |
| log (str): Logging using experiment tracking. Default is "none". | |
| disable_gradient_checkpointing (bool): Gradient checkpointing. Default is False. | |
| logging_steps (int): Logging steps. Default is -1. | |
| eval_strategy (str): Evaluation strategy. Default is "epoch". | |
| save_total_limit (int): Save total limit. Default is 1. | |
| auto_find_batch_size (bool): Auto find batch size. Default is False. | |
| mixed_precision (Optional[str]): Mixed precision (fp16, bf16, or None). Default is None. | |
| lr (float): Learning rate. Default is 3e-5. | |
| epochs (int): Number of training epochs. Default is 1. | |
| batch_size (int): Training batch size. Default is 2. | |
| warmup_ratio (float): Warmup proportion. Default is 0.1. | |
| gradient_accumulation (int): Gradient accumulation steps. Default is 4. | |
| optimizer (str): Optimizer. Default is "adamw_torch". | |
| scheduler (str): Scheduler. Default is "linear". | |
| weight_decay (float): Weight decay. Default is 0.0. | |
| max_grad_norm (float): Max gradient norm. Default is 1.0. | |
| seed (int): Seed. Default is 42. | |
| quantization (Optional[str]): Quantization (int4, int8, or None). Default is "int4". | |
| target_modules (Optional[str]): Target modules. Default is "all-linear". | |
| merge_adapter (bool): Merge adapter. Default is False. | |
| peft (bool): Use PEFT. Default is False. | |
| lora_r (int): Lora r. Default is 16. | |
| lora_alpha (int): Lora alpha. Default is 32. | |
| lora_dropout (float): Lora dropout. Default is 0.05. | |
| image_column (Optional[str]): Image column. Default is "image". | |
| text_column (str): Text (answer) column. Default is "text". | |
| prompt_text_column (Optional[str]): Prompt (prefix) column. Default is "prompt". | |
| push_to_hub (bool): Push to hub. Default is False. | |
| username (Optional[str]): Hugging Face Username. Default is None. | |
| token (Optional[str]): Huggingface token. Default is None. | |
| """ | |
| model: str = Field("google/paligemma-3b-pt-224", title="Model name") | |
| project_name: str = Field("project-name", title="Output directory") | |
| # data params | |
| data_path: str = Field("data", title="Data path") | |
| train_split: str = Field("train", title="Train data config") | |
| valid_split: Optional[str] = Field(None, title="Validation data config") | |
| # trainer params | |
| trainer: str = Field("vqa", title="Trainer type") # captioning, vqa, segmentation, detection | |
| log: str = Field("none", title="Logging using experiment tracking") | |
| disable_gradient_checkpointing: bool = Field(False, title="Gradient checkpointing") | |
| logging_steps: int = Field(-1, title="Logging steps") | |
| eval_strategy: str = Field("epoch", title="Evaluation strategy") | |
| save_total_limit: int = Field(1, title="Save total limit") | |
| auto_find_batch_size: bool = Field(False, title="Auto find batch size") | |
| mixed_precision: Optional[str] = Field(None, title="fp16, bf16, or None") | |
| lr: float = Field(3e-5, title="Learning rate") | |
| epochs: int = Field(1, title="Number of training epochs") | |
| batch_size: int = Field(2, title="Training batch size") | |
| warmup_ratio: float = Field(0.1, title="Warmup proportion") | |
| gradient_accumulation: int = Field(4, title="Gradient accumulation steps") | |
| optimizer: str = Field("adamw_torch", title="Optimizer") | |
| scheduler: str = Field("linear", title="Scheduler") | |
| weight_decay: float = Field(0.0, title="Weight decay") | |
| max_grad_norm: float = Field(1.0, title="Max gradient norm") | |
| seed: int = Field(42, title="Seed") | |
| # peft | |
| quantization: Optional[str] = Field("int4", title="int4, int8, or None") | |
| target_modules: Optional[str] = Field("all-linear", title="Target modules") | |
| merge_adapter: bool = Field(False, title="Merge adapter") | |
| peft: bool = Field(False, title="Use PEFT") | |
| lora_r: int = Field(16, title="Lora r") | |
| lora_alpha: int = Field(32, title="Lora alpha") | |
| lora_dropout: float = Field(0.05, title="Lora dropout") | |
| # column mappings | |
| image_column: Optional[str] = Field("image", title="Image column") | |
| text_column: str = Field("text", title="Text (answer) column") | |
| prompt_text_column: Optional[str] = Field("prompt", title="Prompt (prefix) column") | |
| # push to hub | |
| push_to_hub: bool = Field(False, title="Push to hub") | |
| username: Optional[str] = Field(None, title="Hugging Face Username") | |
| token: Optional[str] = Field(None, title="Huggingface token") | |