hf_clf_train(
    model_name:str, 
    dataset_name:str='', 
    hf_data_column:str='', 
    hf_label_column:str='',
    num_epochs:int=3, 
    batch_size:int=8, 
    lr:float=5e-5, 
    from_hf:bool=True, 
    hf_token:str='',
    inputs:list=[], 
    labels:list=[], 
    output_dir:str='clf_output',
    use_peft:bool=False, 
    peft_config=None, 
    report_to='none', 
    wandb_api_key:str='',
    ddp:bool=False, 
    zero:bool=False, 
    fp16:bool=False, 
    bf16:bool=False,
    gradient_accumulation_steps:int=1, 
    gradient_checkpointing:bool=False
):
​
model_name
string
required
The name or path of the pre-trained model to use.
​
dataset_name
string
The name of the dataset to be used for training. Defaults to an empty string.
​
hf_data_column
string
The name of the column in the dataset containing the input data. Defaults to an empty string.
​
hf_label_column
string
The name of the column in the dataset containing the labels. Defaults to an empty string.
​
num_epochs
int
The number of training epochs. Defaults to 3.
​
batch_size
int
The batch size for training. Defaults to 8.
​
lr
float
The learning rate for optimization. Defaults to 5e-5.
​
from_hf
boolean
A flag to determine whether to load the dataset from Hugging Face. Defaults to True.
​
hf_token
string
The Hugging Face token required for accessing private datasets or models. Defaults to an empty string.
​
inputs
list
A list of input data for training. Defaults to an empty list.
​
labels
list
A list of labels for training. Defaults to an empty list.
​
output_dir
string
The directory to save the output model and logs. Defaults to 'clf_output'.
​
use_peft
boolean
A flag to enable Parameter-Efficient Fine-Tuning (PEFT). Defaults to False.
​
peft_config
object
The configuration object for PEFT. Defaults to None.
​
report_to
string
The service to report training logs to (e.g., wandb). Defaults to 'none'.
​
wandb_api_key
string
The API key for Weights and Biases (WandB) logging. Defaults to an empty string.
​
ddp
boolean
A flag to enable Distributed Data Parallel (DDP) training. Defaults to False.
​
zero
boolean
A flag to enable ZeRO (Zero Redundancy Optimizer) for memory optimization. Defaults to False.
​
fp16
boolean
A flag to enable 16-bit floating-point (FP16) training. Defaults to False.
​
bf16
boolean
A flag to enable 16-bit Brain Floating Point (BF16) training. Defaults to False.
​
gradient_accumulation_steps
int
The number of steps for gradient accumulation. Defaults to 1.
​
gradient_checkpointing
boolean
A flag to enable gradient checkpointing for reducing memory usage. Defaults to False.