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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")
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