Sft Trainer

Supervised fine-tuning trainer module for GLLM training.

This module provides classes and utilities for supervised fine-tuning (SFT) of language models, including training orchestration and trainer abstractions.

SFTTrainer(model_name, datasets_path=None, train_filename=None, validation_filename=None, prompt_filename=None, spreadsheet_id=None, train_sheet=None, validation_sheet=None, prompt_sheet=None, prompt_name=None, experiment_id=None, hyperparameters=None, storage_config=None, resume_from_checkpoint=None, topic=None, framework=None, multimodal=None, sft_column_mapping_config=None, save_processed_dataset=True, output_processed_dir=None, dataset_text_field=None)

Bases: BaseTrainer

SFT Trainer for fine-tuning models using various frameworks.

This trainer orchestrates the fine-tuning process, from data preparation to model training and saving. It uses a set of configuration objects to customize the training behavior.

Initialize the SFT Trainer.

This method sets up the environment, initializes the configuration objects, and checks for hardware requirements.

Parameters:

Name	Type	Description	Default
model_name	`str`	Model to fine-tune. Default is "Qwen/Qwen3-1.7b".	required
datasets_path	`str`, *optional*	Path to datasets. Default is None.	None
train_filename	`str`, *optional*	CSV filename for training data. Default is "training_data.csv".	None
validation_filename	`str`, *optional*	CSV filename for validation data. Default is "validation_data.csv".	None
prompt_filename	`str`, *optional*	CSV filename for prompt templates. Default is "prompt_data.csv".	None
spreadsheet_id	`str`, *optional*	Google Sheets spreadsheet ID. Default is None.	None
train_sheet	`str`, *optional*	Name of the training sheet. Default is "training_data".	None
validation_sheet	`str`, *optional*	Name of the validation sheet. Default is "validation_data".	None
prompt_sheet	`str`, *optional*	Name of the prompt sheet. Default is "prompt_data".	None
prompt_name	`str`, *optional*	Name of the prompt template. Default is "prompt_default".	None
experiment_id	`str`, *optional*	ID of the experiment. Default is "sft_experiment_1".	None
hyperparameters	`FinetunedHyperparameters`, *optional*	Configuration for training hyperparameters. Default is a new instance of FinetunedHyperparameters.	None
storage_config	`StorageConfig`, *optional*	Configuration for model storage. Default is a new instance of StorageConfig.	None
resume_from_checkpoint	`bool`, *optional*	Whether to resume from checkpoint. Default is taken from hyperparameters.resume_from_checkpoint.	None
topic	`str`, *optional*	Topic of the experiment. Default is "SLM_FINETUNING".	None
framework	`str`, *optional*	Fine-tuning framework to use. Default is "unsloth".	None
multimodal	`bool`, *optional*	Whether the model is multimodal. Default is False.	None
sft_column_mapping_config	`SFTColumnMappingConfig`, *optional*	Configuration for column mappings. Default is a new instance of SFTColumnMappingConfig.	None
save_processed_dataset	`bool`, *optional*, defaults to True	Whether to save processed dataset. Default is True.	True
output_processed_dir	`str`, *optional*	Output directory for processed dataset. Default is "data/dataset".	None
dataset_text_field	`str`, *optional*	Name of the field in the dataset that contains the text to be finetuned. Default is "prompt".	None

save_existing_model(model_path)

Save an existing model.

Parameters:

Name	Type	Description	Default
model_path	str	Path to the model directory.	required

save_model(results=None, model_path=None)

Saves model artifacts, handling both trained and existing models.

This method provides a unified interface for: 1. Saving a newly trained model (when results is provided) 2. Uploading an existing model (when model_path is provided)

Parameters:

Name	Type	Description	Default
results	dict	Training results for newly trained models.	None
model_path	str	Path to the model directory.	None

Returns:

Name	Type	Description
str		Path where the model was saved or uploaded.

Raises:

Type	Description
ValueError	If neither model_path nor results is provided, or if configuration is invalid.
RuntimeError	If the save operation fails.

train()

Runs the complete model fine-tuning and evaluation process.

This method orchestrates the entire workflow, initializing components, running the training loop, and saving the results.

Returns:

Type	Description
dict[str, Any]	dict[str, Any]: A dictionary containing training metrics and results, such as training loss, evaluation loss, and runtime.

Raises:

Type	Description
ValueError	If required training parameters are missing or invalid.
RuntimeError	If components fail to initialize, the model or trainer is not set up correctly, or if the training process itself fails for any reason.